• Research article
  • Open access
  • Published: 04 June 2021

Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

  • Israel Júnior Borges do Nascimento 1 , 2 ,
  • Dónal P. O’Mathúna 3 , 4 ,
  • Thilo Caspar von Groote 5 ,
  • Hebatullah Mohamed Abdulazeem 6 ,
  • Ishanka Weerasekara 7 , 8 ,
  • Ana Marusic 9 ,
  • Livia Puljak   ORCID: orcid.org/0000-0002-8467-6061 10 ,
  • Vinicius Tassoni Civile 11 ,
  • Irena Zakarija-Grkovic 9 ,
  • Tina Poklepovic Pericic 9 ,
  • Alvaro Nagib Atallah 11 ,
  • Santino Filoso 12 ,
  • Nicola Luigi Bragazzi 13 &
  • Milena Soriano Marcolino 1

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

BMC Infectious Diseases volume  21 , Article number:  525 ( 2021 ) Cite this article

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Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.

Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.


In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.

Peer Review reports

The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].

The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].

Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].

Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.

In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.


Research question.

This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.

Study design

We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.

Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].

We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].

A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].

Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.

Eligibility criteria

Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.

No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.

No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].

Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.

Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.

Information sources

Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.

The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.

Study selection

All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.

Data collection process

We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.

We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).

We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.

The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).

We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.

Quality assessment in individual reviews

Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .

Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.

One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.

Synthesis of results

For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.

For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.

Managing overlapping systematic reviews

Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.

Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.

figure 1

PRISMA flow diagram

Characteristics of included reviews

Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).

All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.

Population and study designs

Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.

Systematic review findings

The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).

figure 2

A meta-analysis of the prevalence of mortality

Clinical symptoms

Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).

figure 3

A meta-analysis of the prevalence of fever

figure 4

A meta-analysis of the prevalence of cough

figure 5

A meta-analysis of the prevalence of dyspnea

figure 6

A meta-analysis of the prevalence of fatigue or myalgia

figure 7

A meta-analysis of the prevalence of headache

figure 8

A meta-analysis of the prevalence of gastrointestinal disorders

figure 9

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].

Therapeutic possibilities

Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].

Laboratory and radiological findings

Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].

Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.

Quality of evidence in individual systematic reviews

Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .

Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).

Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].

This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.

Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].

The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.

Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.

All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.

We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.

The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].

Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].

Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.

Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].

Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.

Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.

Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.

Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.

Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.

Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].

In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.

Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.

In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.

Availability of data and materials

All data collected and analyzed within this study are available from the corresponding author on reasonable request.

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We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.

This research received no external funding.

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Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany

Thilo Caspar von Groote

Department of Sport and Health Science, Technische Universität München, Munich, Germany

Hebatullah Mohamed Abdulazeem

School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, Australia

Ishanka Weerasekara

Department of Physiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka

Cochrane Croatia, University of Split, School of Medicine, Split, Croatia

Ana Marusic, Irena Zakarija-Grkovic & Tina Poklepovic Pericic

Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000, Zagreb, Croatia

Livia Puljak

Cochrane Brazil, Evidence-Based Health Program, Universidade Federal de São Paulo, São Paulo, Brazil

Vinicius Tassoni Civile & Alvaro Nagib Atallah

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IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.

Additional file 5: Appendix 5.

A detailed explanation of AMSTAR scoring for each item in each review.

Additional file 6: Appendix 6.

List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).

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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4

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covid 19 introduction for thesis

Coronavirus disease 2019 (COVID-19): A literature review


  • 1 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
  • 2 Division of Infectious Diseases, AichiCancer Center Hospital, Chikusa-ku Nagoya, Japan. Electronic address: [email protected].
  • 3 Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
  • 4 Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
  • 5 School of Medicine, The University of Western Australia, Perth, Australia. Electronic address: [email protected].
  • 6 Siem Reap Provincial Health Department, Ministry of Health, Siem Reap, Cambodia. Electronic address: [email protected].
  • 7 Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia; Department of Medical Microbiology and Immunology, University of California, Davis, CA, USA. Electronic address: [email protected].
  • 8 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Clinical Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
  • 9 Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, MI 48109, USA. Electronic address: [email protected].
  • 10 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
  • PMID: 32340833
  • PMCID: PMC7142680
  • DOI: 10.1016/j.jiph.2020.03.019

In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.

Keywords: 2019-nCoV; COVID-19; Novel coronavirus; Outbreak; SARS-CoV-2.

Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

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  • Disease Outbreaks* / prevention & control
  • Pneumonia, Viral* / epidemiology
  • Pneumonia, Viral* / immunology
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  • COVID-19 pandemic and its impact on social relationships and health
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  • http://orcid.org/0000-0003-1512-4471 Emily Long 1 ,
  • Susan Patterson 1 ,
  • Karen Maxwell 1 ,
  • Carolyn Blake 1 ,
  • http://orcid.org/0000-0001-7342-4566 Raquel Bosó Pérez 1 ,
  • Ruth Lewis 1 ,
  • Mark McCann 1 ,
  • Julie Riddell 1 ,
  • Kathryn Skivington 1 ,
  • Rachel Wilson-Lowe 1 ,
  • http://orcid.org/0000-0002-4409-6601 Kirstin R Mitchell 2
  • 1 MRC/CSO Social and Public Health Sciences Unit , University of Glasgow , Glasgow , UK
  • 2 MRC/CSO Social and Public Health Sciences Unit, Institute of Health & Wellbeing , University of Glasgow , Glasgow , UK
  • Correspondence to Dr Emily Long, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G3 7HR, UK; emily.long{at}glasgow.ac.uk

This essay examines key aspects of social relationships that were disrupted by the COVID-19 pandemic. It focuses explicitly on relational mechanisms of health and brings together theory and emerging evidence on the effects of the COVID-19 pandemic to make recommendations for future public health policy and recovery. We first provide an overview of the pandemic in the UK context, outlining the nature of the public health response. We then introduce four distinct domains of social relationships: social networks, social support, social interaction and intimacy, highlighting the mechanisms through which the pandemic and associated public health response drastically altered social interactions in each domain. Throughout the essay, the lens of health inequalities, and perspective of relationships as interconnecting elements in a broader system, is used to explore the varying impact of these disruptions. The essay concludes by providing recommendations for longer term recovery ensuring that the social relational cost of COVID-19 is adequately considered in efforts to rebuild.

  • inequalities

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Data sharing not applicable as no data sets generated and/or analysed for this study. Data sharing not applicable as no data sets generated or analysed for this essay.

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Infectious disease pandemics, including SARS and COVID-19, demand intrapersonal behaviour change and present highly complex challenges for public health. 1 A pandemic of an airborne infection, spread easily through social contact, assails human relationships by drastically altering the ways through which humans interact. In this essay, we draw on theories of social relationships to examine specific ways in which relational mechanisms key to health and well-being were disrupted by the COVID-19 pandemic. Relational mechanisms refer to the processes between people that lead to change in health outcomes.

At the time of writing, the future surrounding COVID-19 was uncertain. Vaccine programmes were being rolled out in countries that could afford them, but new and more contagious variants of the virus were also being discovered. The recovery journey looked long, with continued disruption to social relationships. The social cost of COVID-19 was only just beginning to emerge, but the mental health impact was already considerable, 2 3 and the inequality of the health burden stark. 4 Knowledge of the epidemiology of COVID-19 accrued rapidly, but evidence of the most effective policy responses remained uncertain.

The initial response to COVID-19 in the UK was reactive and aimed at reducing mortality, with little time to consider the social implications, including for interpersonal and community relationships. The terminology of ‘social distancing’ quickly became entrenched both in public and policy discourse. This equation of physical distance with social distance was regrettable, since only physical proximity causes viral transmission, whereas many forms of social proximity (eg, conversations while walking outdoors) are minimal risk, and are crucial to maintaining relationships supportive of health and well-being.

The aim of this essay is to explore four key relational mechanisms that were impacted by the pandemic and associated restrictions: social networks, social support, social interaction and intimacy. We use relational theories and emerging research on the effects of the COVID-19 pandemic response to make three key recommendations: one regarding public health responses; and two regarding social recovery. Our understanding of these mechanisms stems from a ‘systems’ perspective which casts social relationships as interdependent elements within a connected whole. 5

Social networks

Social networks characterise the individuals and social connections that compose a system (such as a workplace, community or society). Social relationships range from spouses and partners, to coworkers, friends and acquaintances. They vary across many dimensions, including, for example, frequency of contact and emotional closeness. Social networks can be understood both in terms of the individuals and relationships that compose the network, as well as the overall network structure (eg, how many of your friends know each other).

Social networks show a tendency towards homophily, or a phenomenon of associating with individuals who are similar to self. 6 This is particularly true for ‘core’ network ties (eg, close friends), while more distant, sometimes called ‘weak’ ties tend to show more diversity. During the height of COVID-19 restrictions, face-to-face interactions were often reduced to core network members, such as partners, family members or, potentially, live-in roommates; some ‘weak’ ties were lost, and interactions became more limited to those closest. Given that peripheral, weaker social ties provide a diversity of resources, opinions and support, 7 COVID-19 likely resulted in networks that were smaller and more homogenous.

Such changes were not inevitable nor necessarily enduring, since social networks are also adaptive and responsive to change, in that a disruption to usual ways of interacting can be replaced by new ways of engaging (eg, Zoom). Yet, important inequalities exist, wherein networks and individual relationships within networks are not equally able to adapt to such changes. For example, individuals with a large number of newly established relationships (eg, university students) may have struggled to transfer these relationships online, resulting in lost contacts and a heightened risk of social isolation. This is consistent with research suggesting that young adults were the most likely to report a worsening of relationships during COVID-19, whereas older adults were the least likely to report a change. 8

Lastly, social connections give rise to emergent properties of social systems, 9 where a community-level phenomenon develops that cannot be attributed to any one member or portion of the network. For example, local area-based networks emerged due to geographic restrictions (eg, stay-at-home orders), resulting in increases in neighbourly support and local volunteering. 10 In fact, research suggests that relationships with neighbours displayed the largest net gain in ratings of relationship quality compared with a range of relationship types (eg, partner, colleague, friend). 8 Much of this was built from spontaneous individual interactions within local communities, which together contributed to the ‘community spirit’ that many experienced. 11 COVID-19 restrictions thus impacted the personal social networks and the structure of the larger networks within the society.

Social support

Social support, referring to the psychological and material resources provided through social interaction, is a critical mechanism through which social relationships benefit health. In fact, social support has been shown to be one of the most important resilience factors in the aftermath of stressful events. 12 In the context of COVID-19, the usual ways in which individuals interact and obtain social support have been severely disrupted.

One such disruption has been to opportunities for spontaneous social interactions. For example, conversations with colleagues in a break room offer an opportunity for socialising beyond one’s core social network, and these peripheral conversations can provide a form of social support. 13 14 A chance conversation may lead to advice helpful to coping with situations or seeking formal help. Thus, the absence of these spontaneous interactions may mean the reduction of indirect support-seeking opportunities. While direct support-seeking behaviour is more effective at eliciting support, it also requires significantly more effort and may be perceived as forceful and burdensome. 15 The shift to homeworking and closure of community venues reduced the number of opportunities for these spontaneous interactions to occur, and has, second, focused them locally. Consequently, individuals whose core networks are located elsewhere, or who live in communities where spontaneous interaction is less likely, have less opportunity to benefit from spontaneous in-person supportive interactions.

However, alongside this disruption, new opportunities to interact and obtain social support have arisen. The surge in community social support during the initial lockdown mirrored that often seen in response to adverse events (eg, natural disasters 16 ). COVID-19 restrictions that confined individuals to their local area also compelled them to focus their in-person efforts locally. Commentators on the initial lockdown in the UK remarked on extraordinary acts of generosity between individuals who belonged to the same community but were unknown to each other. However, research on adverse events also tells us that such community support is not necessarily maintained in the longer term. 16

Meanwhile, online forms of social support are not bound by geography, thus enabling interactions and social support to be received from a wider network of people. Formal online social support spaces (eg, support groups) existed well before COVID-19, but have vastly increased since. While online interactions can increase perceived social support, it is unclear whether remote communication technologies provide an effective substitute from in-person interaction during periods of social distancing. 17 18 It makes intuitive sense that the usefulness of online social support will vary by the type of support offered, degree of social interaction and ‘online communication skills’ of those taking part. Youth workers, for instance, have struggled to keep vulnerable youth engaged in online youth clubs, 19 despite others finding a positive association between amount of digital technology used by individuals during lockdown and perceived social support. 20 Other research has found that more frequent face-to-face contact and phone/video contact both related to lower levels of depression during the time period of March to August 2020, but the negative effect of a lack of contact was greater for those with higher levels of usual sociability. 21 Relatedly, important inequalities in social support exist, such that individuals who occupy more socially disadvantaged positions in society (eg, low socioeconomic status, older people) tend to have less access to social support, 22 potentially exacerbated by COVID-19.

Social and interactional norms

Interactional norms are key relational mechanisms which build trust, belonging and identity within and across groups in a system. Individuals in groups and societies apply meaning by ‘approving, arranging and redefining’ symbols of interaction. 23 A handshake, for instance, is a powerful symbol of trust and equality. Depending on context, not shaking hands may symbolise a failure to extend friendship, or a failure to reach agreement. The norms governing these symbols represent shared values and identity; and mutual understanding of these symbols enables individuals to achieve orderly interactions, establish supportive relationship accountability and connect socially. 24 25

Physical distancing measures to contain the spread of COVID-19 radically altered these norms of interaction, particularly those used to convey trust, affinity, empathy and respect (eg, hugging, physical comforting). 26 As epidemic waves rose and fell, the work to negotiate these norms required intense cognitive effort; previously taken-for-granted interactions were re-examined, factoring in current restriction levels, own and (assumed) others’ vulnerability and tolerance of risk. This created awkwardness, and uncertainty, for example, around how to bring closure to an in-person interaction or convey warmth. The instability in scripted ways of interacting created particular strain for individuals who already struggled to encode and decode interactions with others (eg, those who are deaf or have autism spectrum disorder); difficulties often intensified by mask wearing. 27

Large social gatherings—for example, weddings, school assemblies, sporting events—also present key opportunities for affirming and assimilating interactional norms, building cohesion and shared identity and facilitating cooperation across social groups. 28 Online ‘equivalents’ do not easily support ‘social-bonding’ activities such as singing and dancing, and rarely enable chance/spontaneous one-on-one conversations with peripheral/weaker network ties (see the Social networks section) which can help strengthen bonds across a larger network. The loss of large gatherings to celebrate rites of passage (eg, bar mitzvah, weddings) has additional relational costs since these events are performed by and for communities to reinforce belonging, and to assist in transitioning to new phases of life. 29 The loss of interaction with diverse others via community and large group gatherings also reduces intergroup contact, which may then tend towards more prejudiced outgroup attitudes. While online interaction can go some way to mimicking these interaction norms, there are key differences. A sense of anonymity, and lack of in-person emotional cues, tends to support norms of polarisation and aggression in expressing differences of opinion online. And while online platforms have potential to provide intergroup contact, the tendency of much social media to form homogeneous ‘echo chambers’ can serve to further reduce intergroup contact. 30 31

Intimacy relates to the feeling of emotional connection and closeness with other human beings. Emotional connection, through romantic, friendship or familial relationships, fulfils a basic human need 32 and strongly benefits health, including reduced stress levels, improved mental health, lowered blood pressure and reduced risk of heart disease. 32 33 Intimacy can be fostered through familiarity, feeling understood and feeling accepted by close others. 34

Intimacy via companionship and closeness is fundamental to mental well-being. Positively, the COVID-19 pandemic has offered opportunities for individuals to (re)connect and (re)strengthen close relationships within their household via quality time together, following closure of many usual external social activities. Research suggests that the first full UK lockdown period led to a net gain in the quality of steady relationships at a population level, 35 but amplified existing inequalities in relationship quality. 35 36 For some in single-person households, the absence of a companion became more conspicuous, leading to feelings of loneliness and lower mental well-being. 37 38 Additional pandemic-related relational strain 39 40 resulted, for some, in the initiation or intensification of domestic abuse. 41 42

Physical touch is another key aspect of intimacy, a fundamental human need crucial in maintaining and developing intimacy within close relationships. 34 Restrictions on social interactions severely restricted the number and range of people with whom physical affection was possible. The reduction in opportunity to give and receive affectionate physical touch was not experienced equally. Many of those living alone found themselves completely without physical contact for extended periods. The deprivation of physical touch is evidenced to take a heavy emotional toll. 43 Even in future, once physical expressions of affection can resume, new levels of anxiety over germs may introduce hesitancy into previously fluent blending of physical and verbal intimate social connections. 44

The pandemic also led to shifts in practices and norms around sexual relationship building and maintenance, as individuals adapted and sought alternative ways of enacting sexual intimacy. This too is important, given that intimate sexual activity has known benefits for health. 45 46 Given that social restrictions hinged on reducing household mixing, possibilities for partnered sexual activity were primarily guided by living arrangements. While those in cohabiting relationships could potentially continue as before, those who were single or in non-cohabiting relationships generally had restricted opportunities to maintain their sexual relationships. Pornography consumption and digital partners were reported to increase since lockdown. 47 However, online interactions are qualitatively different from in-person interactions and do not provide the same opportunities for physical intimacy.

Recommendations and conclusions

In the sections above we have outlined the ways in which COVID-19 has impacted social relationships, showing how relational mechanisms key to health have been undermined. While some of the damage might well self-repair after the pandemic, there are opportunities inherent in deliberative efforts to build back in ways that facilitate greater resilience in social and community relationships. We conclude by making three recommendations: one regarding public health responses to the pandemic; and two regarding social recovery.

Recommendation 1: explicitly count the relational cost of public health policies to control the pandemic

Effective handling of a pandemic recognises that social, economic and health concerns are intricately interwoven. It is clear that future research and policy attention must focus on the social consequences. As described above, policies which restrict physical mixing across households carry heavy and unequal relational costs. These include for individuals (eg, loss of intimate touch), dyads (eg, loss of warmth, comfort), networks (eg, restricted access to support) and communities (eg, loss of cohesion and identity). Such costs—and their unequal impact—should not be ignored in short-term efforts to control an epidemic. Some public health responses—restrictions on international holiday travel and highly efficient test and trace systems—have relatively small relational costs and should be prioritised. At a national level, an earlier move to proportionate restrictions, and investment in effective test and trace systems, may help prevent escalation of spread to the point where a national lockdown or tight restrictions became an inevitability. Where policies with relational costs are unavoidable, close attention should be paid to the unequal relational impact for those whose personal circumstances differ from normative assumptions of two adult families. This includes consideration of whether expectations are fair (eg, for those who live alone), whether restrictions on social events are equitable across age group, religious/ethnic groupings and social class, and also to ensure that the language promoted by such policies (eg, households; families) is not exclusionary. 48 49 Forethought to unequal impacts on social relationships should thus be integral to the work of epidemic preparedness teams.

Recommendation 2: intelligently balance online and offline ways of relating

A key ingredient for well-being is ‘getting together’ in a physical sense. This is fundamental to a human need for intimate touch, physical comfort, reinforcing interactional norms and providing practical support. Emerging evidence suggests that online ways of relating cannot simply replace physical interactions. But online interaction has many benefits and for some it offers connections that did not exist previously. In particular, online platforms provide new forms of support for those unable to access offline services because of mobility issues (eg, older people) or because they are geographically isolated from their support community (eg, lesbian, gay, bisexual, transgender and queer (LGBTQ) youth). Ultimately, multiple forms of online and offline social interactions are required to meet the needs of varying groups of people (eg, LGBTQ, older people). Future research and practice should aim to establish ways of using offline and online support in complementary and even synergistic ways, rather than veering between them as social restrictions expand and contract. Intelligent balancing of online and offline ways of relating also pertains to future policies on home and flexible working. A decision to switch to wholesale or obligatory homeworking should consider the risk to relational ‘group properties’ of the workplace community and their impact on employees’ well-being, focusing in particular on unequal impacts (eg, new vs established employees). Intelligent blending of online and in-person working is required to achieve flexibility while also nurturing supportive networks at work. Intelligent balance also implies strategies to build digital literacy and minimise digital exclusion, as well as coproducing solutions with intended beneficiaries.

Recommendation 3: build stronger and sustainable localised communities

In balancing offline and online ways of interacting, there is opportunity to capitalise on the potential for more localised, coherent communities due to scaled-down travel, homeworking and local focus that will ideally continue after restrictions end. There are potential economic benefits after the pandemic, such as increased trade as home workers use local resources (eg, coffee shops), but also relational benefits from stronger relationships around the orbit of the home and neighbourhood. Experience from previous crises shows that community volunteer efforts generated early on will wane over time in the absence of deliberate work to maintain them. Adequately funded partnerships between local government, third sector and community groups are required to sustain community assets that began as a direct response to the pandemic. Such partnerships could work to secure green spaces and indoor (non-commercial) meeting spaces that promote community interaction. Green spaces in particular provide a triple benefit in encouraging physical activity and mental health, as well as facilitating social bonding. 50 In building local communities, small community networks—that allow for diversity and break down ingroup/outgroup views—may be more helpful than the concept of ‘support bubbles’, which are exclusionary and less sustainable in the longer term. Rigorously designed intervention and evaluation—taking a systems approach—will be crucial in ensuring scale-up and sustainability.

The dramatic change to social interaction necessitated by efforts to control the spread of COVID-19 created stark challenges but also opportunities. Our essay highlights opportunities for learning, both to ensure the equity and humanity of physical restrictions, and to sustain the salutogenic effects of social relationships going forward. The starting point for capitalising on this learning is recognition of the disruption to relational mechanisms as a key part of the socioeconomic and health impact of the pandemic. In recovery planning, a general rule is that what is good for decreasing health inequalities (such as expanding social protection and public services and pursuing green inclusive growth strategies) 4 will also benefit relationships and safeguard relational mechanisms for future generations. Putting this into action will require political will.

Ethics statements

Patient consent for publication.

Not required.

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Twitter @karenmaxSPHSU, @Mark_McCann, @Rwilsonlowe, @KMitchinGlasgow

Contributors EL and KM led on the manuscript conceptualisation, review and editing. SP, KM, CB, RBP, RL, MM, JR, KS and RW-L contributed to drafting and revising the article. All authors assisted in revising the final draft.

Funding The research reported in this publication was supported by the Medical Research Council (MC_UU_00022/1, MC_UU_00022/3) and the Chief Scientist Office (SPHSU11, SPHSU14). EL is also supported by MRC Skills Development Fellowship Award (MR/S015078/1). KS and MM are also supported by a Medical Research Council Strategic Award (MC_PC_13027).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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  • Review Article
  • Published: 17 July 2020

A literature review of 2019 novel coronavirus (SARS-CoV2) infection in neonates and children

  • Matteo Di Nardo 1 ,
  • Grace van Leeuwen 2 ,
  • Alessandra Loreti 3 ,
  • Maria Antonietta Barbieri 4 ,
  • Yit Guner 5 ,
  • Franco Locatelli 6 &
  • Vito Marco Ranieri 7  

Pediatric Research volume  89 ,  pages 1101–1108 ( 2021 ) Cite this article

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At the time of writing, there are already millions of documented infections worldwide by the novel coronavirus 2019 (2019-nCoV or severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)), with hundreds of thousands of deaths. The great majority of fatal events have been recorded in adults older than 70 years; of them, a large proportion had comorbidities. Since data regarding the epidemiologic and clinical characteristics in neonates and children developing coronavirus disease 2019 (COVID-19) are scarce and originate mainly from one country (China), we reviewed all the current literature from 1 December 2019 to 7 May 2020 to provide useful information about SARS-CoV2 viral biology, epidemiology, diagnosis, clinical features, treatment, prevention, and hospital organization for clinicians dealing with this selected population.

Children usually develop a mild form of COVID-19, rarely requiring high-intensity medical treatment in pediatric intensive care unit.

Vertical transmission is unlikely, but not completely excluded.

Children with confirmed or suspected COVID-19 must be isolated and healthcare workers should wear appropriate protective equipment.

Some clinical features (higher incidence of fever, vomiting and diarrhea, and a longer incubation period) are more common in children than in adults, as well as some radiologic aspects (more patchy shadow opacities on CT scan images than ground-glass opacities).

Supportive and symptomatic treatments (oxygen therapy and antibiotics for preventing/treating bacterial coinfections) are recommended in these patients.

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. 1 Since its first outbreak in Wuhan, in the Hubei province of China in early December 2019, 2 SARS-CoV2 has spread all over the world infecting millions of people and causing hundreds of thousands o deaths [case fatality rate (CFR): 6.25%, John Hopkins Coronavirus Resource Center, accessed 7 May 2020]. 3

Respiratory viral infections, in general, are more frequent and severe in children than in adults. SARS-CoV2, instead, showed a different scenario. Infection rates appear to be similar between children and adults; however, children develop a milder illness with a low CFR (<0.1%). 3 , 4 , 5 , 6 , 7 The reasons for this milder severity in childhood are not yet understood, and the actual epidemiologic and clinical data of infected neonates and children are not sufficient to solve these gaps. Thus, due to the scarcity of data on SARS-CoV2 in children, we aimed at evaluating the current literature available to provide useful information for clinicians dealing with this particular population.

Search strategy

References for this review were identified through searches on PubMED, Ovid MEDLINE, and EMBASE from 1 December 2019 to 7 May 2020, by two highly experienced librarians at Children’s Hospital Bambino Gesù by using relevant terms related to 2019-nCoV, COVID-19, and SARS-CoV2 in neonates and children (Supplementary Material  1 ). Reference lists of the articles identified by this search strategy were also searched. Earlier reports were not excluded, especially if they were highly cited articles. Only articles published in English were included in this review. Three hundred and seventy-four papers were published in PubMed, 117 in Ovid MEDLINE, and 119 in EMBASE. Among them, 73 were deemed relevant to the purposes of this review (PRISMA flowchart Supplementary Material  2 ).

Biological mechanisms of viral infection and lung injury

Coronaviruses are single-strand, positive-sense RNA viruses with spike-like projections on their surface. 8 These viruses can infect both animals and humans. Among human-infecting coronaviruses, four types (HKU1, NL63, 229E, and OC43) are responsible for mild forms of respiratory disease. 9 , 10 SARS-CoV2, SARS-CoV, and the Middle East respiratory syndrome coronavirus (MERS-CoV) are zoonotic viruses and can infect humans, causing severe respiratory infections, only crossing from animals (Fig.  1 ).

figure 1

Summary of coronavirus diseases (adapted from Zimmermann and Curtis 8 ).

SARS-CoV2 infects the host cells through an envelope spike (S) protein that mediates the binding and membrane fusion through the angiotensin-converting enzyme 2 (ACE-2) receptor (Fig.  2a, b ). The spike protein is functionally divided into an S1 domain, responsible for receptor binding, and an S2 domain, responsible for cell membrane fusion. 11 SARS-CoV2 employs the transmembrane serine protease 2 of the host cell to prime the S protein and bind the ACE-2 receptor. Other transmembrane pore-forming viral proteins (viroporins) can trigger the NLRP3 (NOD-like receptor 3 inflammasome)-inducing pyroptosis in the host cell. 12

figure 2

a Renin–angiotensin system (RAS): normal physiology. Renin converts angiotensinogen in angiotensin 1 (ANG 1). Angiotensin-converting enzyme (ACE) converts ANG1 in angiotensin 2 (ANG2). Angiotensin-converting enzyme 2 (ACE-2), a homolog of ACE, is a monocarboxypeptidase that converts ANG2 into angiotensin 1–7 (ANG1–7), which, by virtue of its actions on the MasR (mitocondrial assembly receptor), opposes the molecular and cellular effects of ANG2. ANG2 promotes vasoconstriction, inflammation, and oxidative stress via the activation of AT1R (angiotensin 2 receptor 1). b  SARS-CoV2 host cell entry mechanism: Spike protein (S1) binds the ACE-2 receptor once primed by the transmembrane protease serine 2 inhibitor (TMPRSS2). This binding leads to viral entry and replication and induces mechanisms of lung injury. c  Potential therapeutic strategies against SARS-COV2. Spike protein-based vaccine; TMPRSS2 inhibitors to block the priming of the spike protein; surface ACE-2 receptor blocker; soluble form of ACE-2 receptor compete with the binding of SARS-CoV2 to the surface ACE-2 receptor.

ACE-2 receptors are expressed in many tissues; however, the majority are present on the alveolar epithelial type II cells. 13 In addition, gene ontology enrichment analysis showed that the ACE-2-expressing epithelial cells have high levels of multiple viral process-related genes, including regulatory genes for viral processes, life cycle, assembly, and genome replication. 13 All these features strongly support the hypothesis that the ACE-2 receptor mediates SARS-CoV2 replication in the lung. SARS-CoV2, through the binding to the ACE-2 receptor, downregulates the ACE-2 intracellular signaling (mitochondrial assembly receptor), causing inflammation, vasoconstriction, and fibrosis in the lung. 13

Epidemiology and pathogenesis in neonates and children

Published data and anecdotal reports support the notion that the number of children found to be infected by SARS-CoV2 is small and their clinical manifestations of COVID-19 are milder compared to adults. 4 , 5 , 6 , 14 , 15 , 16 , 17 , 18

The incidence of SARS-CoV2 confirmed that pediatric cases are low and variable among countries (China: 2–12.3%, 4 , 5 Italy: 1.2%, 19 Korea: 4.8%, 20 USA: 5% 21 ). Several reasons justify this variable incidence: testing availability, testing policy 22 , 23 (at the beginning of pandemics some countries tested only children with established contact with a person with COVID-19, then only hospitalized children with symptoms), and the fact that the infection in children is mild or without symptoms. 24 , 25 Available data also suggest that all ages (0–18) can be infected, but infants seem to be most vulnerable. 5 , 26

Human-to-human transmission (mainly family clustered) is the major transmission mode. 4 , 5 , 27 Children can be infected by inhalation of large droplets generated during coughing or sneezing or by contact with contaminated surface (fomite). 9 , 10 , 28 , 29 , 30 As the virus can be also released in the stool, the fecal–oral transmission cannot be ruled out. 31 , 32 , 33 , 34 Similar to SARS-CoV and MERS-CoV, nosocomial transmission of SARS-CoV2 is high, 9 , 10 , 35 , 36 although no cases of nosocomial infections have been described in children during hospital recovery.

Despite the absence of clinical features of infection or positive microbiological findings in neonates born from SARS-CoV2-positive mothers, 14 , 18 , 37 , 38 , 39 , 40 , 41 , 42 vertical maternal–fetal transmission cannot be ruled out completely. 43 , 44 Conversely, SARS-CoV2 has not been isolated from cord blood, amniotic fluid, and breast milk to date. However, it is crucial to screen pregnant women, implement strict infection control measures on those who tested positive, and monitor the neonates at risk. 44 , 45

Since the incubation period (median 5–7 days) in children and young adolescent varies from 2 to 14 days, but is generally longer than in adults, 10 , 46 , 47 , 48 dynamic observation is mandatory for suspected children. 49 , 50 The median period from symptom onset to hospital admission for patients who were hospitalized is 2 days (1.00–3.50). Recovery generally happens in 1–2 weeks after onset. 40 , 48 Both symptomatic patients and asymptomatic carriers can transmit SARS-CoV2. 49 , 51 , 52

The basic case reproduction (R0) of SARS-CoV2 is variable (2–3.5 in the early stage of the disease); 9 however, the R0 of SARS-CoV2 is higher than SARS-CoV and H1N1. 10 The CFR is ~6.25% (data from 7 May, John Hopkins Coronavirus Resource Center) 3 and varies among countries, 53 patients’ age, and is influenced by testing availability. 54 CFR of patients below 18 years is below <0.1% (adapted from John Hopkins Coronavirus Resource center at 7 May 2020). 3 , 7

This age specificity is still not completely understood. 24 , 55 It is speculated that children, as compared with adults, may have a higher expression of ACE-2 receptors in the type II lung pneumocytes, protecting them from the severe clinical manifestation of COVID-19 (low cytokine release, low pulmonary vascular permeability, etc.). 55 Other immunologic mechanisms (trained immunity, an early and high polyclonal B cell response to SARS-CoV2 with the production of substantial numbers of plasmablasts, and an high level natural killer cells) could also contribute to explain this age-specific characteristic. 55 , 56 A less intense mechanism of antibody-dependent enhancement, instead, could explain why COVID-19 clinical features are milder in children than in adults. 12

Since the World Health Organization (WHO) recently declared COVID-19 a pandemic on 11 March 2020, every patient presenting with evidence of fever, respiratory symptoms, gastrointestinal symptoms, or fatigue should be considered potentially infected (suspected case) with SARS-CoV-2.

Diagnosis of COVID-19 is made by using real-time polymerase chain reaction (RT-PCR) on samples from nasopharyngeal, oropharyngeal swabs, and lower respiratory tract samples whenever possible. 4 , 5 Negative nasopharyngeal swab is generally re-tested after 24 h due to the low negative predictive value of this testing. 57 SARS-CoV2 can be also detected on stools. 33 , 58 , 59 A “positive” RT-PCR result reflects only the detection of viral RNA and does not necessarily indicate the presence of a viable virus. 52

Confirmed cases are defined by positive molecular tests, while asymptomatic cases are defined by positive molecular tests without symptoms.

In children, more than in adults, COVID-19 poses important diagnostic challenges due to the longer incubation period that includes a prolonged interval (~5–6 days) of viral shedding prior to the onset of symptoms. 51 , 60 Moreover, the duration of asymptomatic shedding is not only variable, but also differs according to the anatomic level (upper versus lower airways) of the infection. 49 , 50

At present, among adult patients in affected areas, the most common cause of viral pneumonia with unclear etiology is SARS-CoV2; 2 conversely, in children several other pathogens (influenza, para-influenza, adenovirus, respiratory syncytial virus, metapneumovirus, or other human coronaviruses) can produce very similar clinical and radiologic findings and should be considered in the differential diagnosis. 6 , 8 , 26 , 61 Atypical microorganisms, such as chlamydia pneumoniae and mycoplasma, must be also excluded. 10

No laboratory investigations and radiological findings are diagnostic of SARS-CoV2. 4 , 5 , 6 , 10 , 47 , 62

Clinical features

Clinical manifestations of COVID-19 in neonates and children reported are generally mild and similar among countries. 4 , 5 , 6 , 14 , 16 , 22 , 23 , 37 , 38 , 46 , 63 , 64 , 65 Most commonly, at hospital admission, children presented with fever and respiratory symptoms with cough, sore throat, pharyngeal erythema, nasal congestion, tachypnea/dyspnea, and tachycardia. 22 , 23 , 65 Often, gastrointestinal symptoms, including abdominal pain, nausea, vomiting, and diarrhea, were the first manifestations. 4 , 5 , 15 , 46 , 64 , 66 Neurological manifestations such as seizures, dystonia, and altered mental status were rare. 66 Neonates, instead, showed tachypnea, cough, grunting, nasal flaring, vomiting, poor feeding, diarrhea, and lethargy. 45 , 61 , 67 , 68 , 69 Hospital admission was higher in Italy and Spain than in China and USA; 4 , 21 , 22 , 65 however, this was mainly due to local policies (testing availability and policy, need of patient isolation) rather than clinical condition. 22 , 65

In the largest retrospective cohort of COVID-19 pediatric patients reported so far [2134 patients including 731 (34.1%) laboratory-confirmed and 1412 (65.9%) suspected cases], Dong et al. 5 defined the severity of COVID-19 in asymptomatic infection, mild, moderate, severe, and critical cases, based on the clinical features, laboratory testing, and X-ray imaging (Table  1 ). In this cohort, 4.4% of infected children were asymptomatic, while the remaining children presented a mild (50.9%) or moderate disease (38.8%), respectively. Only 5.2% had severe disease, while 0.6% had critical disease. The proportion of severe and critical cases was 10.6%, 7.3%, 4.2%, 4.1%, and 3.0% for the age group of <1, 1–5, 6–10, 11–15, and >16 years, respectively.

Lu et al. 4 showed 15.8% of COVID-19 children included in their retrospective cohort (171 SARS-CoV2 confirmed cases) were completely asymptomatic and did not show any radiological findings of pneumonia.

Respiratory coinfections were present in almost half of the cases. 4 , 5 , 26 Comorbidities, as in adult patients, 70 may affect outcome 23 and the likelihood of Pediatric Intensive Care Unit (PICU) admission. 4 , 23

In adults, the incidence of ICU admission was high and variable among countries (5% in China and 9% in Italy); 70 , 71 in children, the incidence was lower (0.21–5.2% among Chinese PICUs, 4 , 5 , 15 0.04% in USA 23 ). Of note, several biases (retrospective nature of these studies, 5 , 61 the proportion of the detected cases, the use of different PICU admission criteria among centers, 5 the use of the same data source with overlapping data—Chinese Centers for Disease Control and Prevention database—and the high number of suspected cases 47 ) could have affected the interpretation of these results.

Most of the laboratory abnormalities in children with COVID-19 are nonspecific. Henry et al. 62 reviewed the data of 66 children from 12 different studies and found that 69.2% of children had normal leukocyte counts and that neutrophilia or neutropenia were rare (<5%). Platelet count was variable among studies (generally higher than the normal range), while C-reactive protein and procalcitonin were increased in 13.6% and 10.6% of the cases, respectively. 62

Children admitted to the PICU 15 showed normal or increased whole blood counts (7/8) and increased C-reactive protein, procalcitonin, and lactate dehydrogenase (6/8). High levels of pro-inflammatory and anti-inflammatory cytokines were also present similarly to the adult patients. 72 , 73

Although lymphocytopenia is very common in adults with severe COVID-19 and associated with worse outcomes, 47 it is less common in children (2–3.5%), likely due to the constitutional high percentage of lymphocytes typical of this age. 62 , 74 In adult patients, high ferritin, high d -dimers, and coagulopathy were associated with poor prognosis, 70 but these laboratory findings were rare in children; high d -dimers levels were found in one of the two patients who died from COVID-19. 4 , 15 However, during April 2020, a surge of anecdotal cases showing a hyper-inflammatory state (pediatric multisystem inflammatory syndrome temporally associated with COVID-19) and features similar to atypical Kawasaki disease or Kawasaki disease shock syndrome were reported in Europe (United Kingdom, Spain, Italy). 75 , 76 Many of these patients had positive SARS-CoV2 antibodies and presented an inflammatory state (elevated concentration of C-reactive protein, procalcitonin, ferritin triglycerides, and d -dimers) with cutaneous rash, peripheral edema, conjunctivitis, myocardial dysfunction (elevated cardiac enzymes), and coronary vessels inflammation.

Radiologic findings of SARS-CoV2 viral pneumonia were also variable among children (Fig.  3 ). 4 At hospital admission, many children presented a chest X-ray showing an interstitial pneumonia, 26 while chest computed tomography (CT) scan showed patchy shadows (unilateral and bilateral) with opacities of high density. The typical adult feature of ground-glass opacity was less frequent at hospital admission (32.7%); 4 instead, it was more common in patients admitted to the PICU for respiratory failure. 4 , 5 , 6 , 26 , 77 , 78 , 79 Bedside lung ultrasonography was also used as a diagnostic tool in the emergency departments in a minority of patients; 80 90% of these received a diagnosis of interstitial lung syndrome without further radiographic imaging. 65

figure 3

a Chest X ray and b chest computed tomography. Vital signs: respiratory rate 22 breaths/min, SpO 2 : 97% in room air. The patient was supported with high-flow nasal cannula 25 L/min, FiO 2 : 30% in the pediatric ward.

Treatment of COVID-19 in neonates and children mainly relies on supportive care. 4 , 10

Home isolation is the first step to manage children with mild symptoms and no underlying chronic conditions. Hospitalization may be considered if rapid deterioration is anticipated or if the patient is not able to urgently return to hospital when signs and symptoms of complicated disease arise. Moderate cases should be managed in hospital, monitoring vital signs and oxygen saturation. Supportive care for these children includes temperature control with antipyretics, bed rest, hydration, and good nutrition. Routine antibiotics and antifungal drugs must be avoided and used only when coinfections are proven or strongly suspected. 10 , 15

In hypoxic patients, oxygen therapy should be immediately initiated. 81 Several devices [low flow nasal cannula, high-flow nasal cannula (HFNC), and noninvasive ventilation (NIV)] can be used according to the centers’ experience. Caution must be taken, since all noninvasive techniques bear the risk of aerosol contamination; strict personal protection equipment (PPE) must be used when caring for these patients.

Invasive mechanical ventilation is indicated if: SpO 2 /FiO 2  < 221 or if there is no improvement in oxygenation (target SpO 2 92–97% with FiO 2  < 0.4) within 30–60 min of HFNC or if there is no improvement in oxygenation (target SpO 2 92–97% and FiO 2  < 0.6) within 60–90 min of CPAP/NIV. 81 Escalating therapies are recommended in case of refractory hypoxia (surfactant therapy in neonates, inhaled nitric oxide, high frequency oscillatory ventilation, and extracorporeal membrane oxygenation). 81 , 82 , 83

A small portion of children with COVID-19 developed septic shock; 5 , 15 , 84 thus, this condition must be always suspected and managed according to the current pediatric guidelines since specific issues for COVID-19 have not been reported so far. 85 Corticosteroids should not be used in pediatric patients, 86 except when required for other indications, such as asthma exacerbations, refractory shock, or evidence of cytokine storm. 16

Several treatment options (intravenous immunoglobulin, interleukin-1 (IL-1) blockade, IL-6 receptor blockade, azythromycin-chloroquine, plasma exchange, infusion of plasma from convalescent subjects, cytokine adsorption filters) have been used in critically ill adult patients; however, data on their efficacy and safety have not been reported yet, thus caution should be used also in children. 87

Antiviral drugs should be used with caution after weighing advantages and disadvantages. For those with mild symptoms, low dosage of interferon-α nebulization has been used 16 in combination with oral ribavirin. Lopinavir/litonavir 15 and remdesivir 88 , 89 have been used in more severe cases; however, their efficacy and safety in children remain to be determined. 90 Remdesivir should be preferred in children because of its positive effects in a recent adult trial; 88 , 89 however, when not available, or when patients are not good candidate to remdesivir, hydroxychloroquine could be considered. 88 The combination of three or more antiviral drugs is generally not recommended. 90

Potential therapeutic strategies for SARS-COV2 are the spike protein-based vaccine, the inhibitors of transmembrane protease serine 2 activity, and the delivery of excessive soluble form of ACE-2 or antibody against the surface of ACE-2 receptors (Fig.  2c ). 13

Prevention and healthcare organization

COVID-19 has no approved treatment in neonates and children and a large-scale vaccine is still under development; thus, prevention is crucial. 10 , 91

SARS-CoV-2 has unique characteristics that makes its prevention complex. SARS-CoV-2 can cause an asymptomatic infection, can be transmitted during the incubation period and after clinical recovery, 13 has a very high affinity to ACE-2 receptors, which are expressed on many mucosal surfaces, resulting in high transmissibility, and can be spread also by fomite. 10

The high transmissibility and low CFR, combined with the discouraging projections of the spread of the virus among adults, 70 fostered many governments, at the beginning of March 2020, to adopt stringent containment and self-isolation measures to reduce the spread of the virus. An intense public health response was started by many countries after the pandemic declaration and involved many strategies: lockdown of the cities and mass quarantine, social distancing mandates, schools closure, cancellation of public gatherings, reduction of domestic and international flights, development of environmental measures and personal protection procedures, and strict contacts tracings by the medical and public health professionals. These measures aim to delay major surges of patients and to lower the demand for hospital extra beds, while protecting the most vulnerable subjects from infection, especially the elderly and those with comorbidities. 92

Data showed that pediatric cases requiring high-intensity medical assistance are uncommon; 5 , 15 however, isolation of all suspected and confirmed patients remains mandatory to avoid the spread of SARS-CoV2 among caregivers and healthcare workers. Therefore, many pediatric hospitals have developed local guidelines and logistic plans (simulations and training courses, reduction of elective surgeries and visits to outpatient clinics, etc.) to identify in advance potential surge capacity in the form of dedicated environment with extra beds for isolation, quarantine, and dedicated staff. As stocks of PPE might run low during a period of pandemic, strict hospital policies should also be adopted according to the WHO guidelines. 93 Furthermore, considering the high number of adult ICU admissions and the difficulties associated to create extra beds in a short period of time, 70 pediatric intensivists and nurses should be ready and prepared to offer help by managing adult patients in PICU 94 or to help in adult ICUs.

Differently from adults, home isolation is not easily performed in children, because they often require the presence of the parents, limiting the use of protective distances (>1.5 m). In those cases, all people sharing a common environment with a SARS-CoV2-positive child should consider the use of gloves and face masks, if available. Hand hygiene practices are extremely important to prevent the spread of the COVID-19 virus at home and in public environments. The WHO recommends washing hands, especially after coughing or sneezing (including sneeze/cough into elbow or tissue), before eating and after using the toilet or sharing common spaces. 95 Hand washing also interrupts transmission of other viruses and bacteria causing common colds, flu, and pneumonia, thus reducing the general burden of disease. Relatives at risk (e.g., people over the age of 65 years, pregnant women, people who are immunocompromised or who have chronic heart, lung, or kidney conditions) 96 should be isolated in protected environment, avoiding exposures to infected children. Because infants cannot wear masks, parents must wear masks, wash hands before close contacts, and sterilize the toys and tablet regularly. 97

All suspected children requiring hospital assistance must be isolated in single rooms (whenever possible, or in dedicated environments, maintaining adequate distances between beds) until the results of the test are available; confirmed patients must be placed in dedicated area for quarantine. A dedicated algorithm must be adopted for the use of the operating theaters in suspected or confirmed COVID-19 cases, according to the urgency of the operation, anticipated viral burden at the surgical site, and the risk that a procedure could spread the virus by aereosol. 98 , 99 Negative pressure rooms are of help, but not mandatory to manage these patients. 10 All rooms and transition environments must be decontaminated after the patient discharge (Fig.  3 ).

Since a high number of health care workers has been infected by SARS-CoV2, all suspected patients, until proven negative, must be assisted by health care providers using PPE 93 and all aerosol generating procedures (intubation, bronchoscopy, tube/tracheostomy suctioning, etc.) must be also performed using airborne transmission precautions. 93

Enhanced traffic control bundling strategies must be adopted by all emergency departments, 100 including a triage zone, transition zones conduction to a quarantine ward or to an isolation ward (Fig.  4 ). A dedicated pathway for children non-SARS-CoV2 suspected (e.g., trauma, poisoning, etc.) must also be created in parallel to avoid contact. Telemedicine should be implemented to help reduce hospital and clinic visits, 101 , 102 by triaging low-acuity patients while delivering high-quality care. 103

figure 4

Enhanced traffic control system used in Children’s Hospital Bambino Gesù, Rome, Italy.

The scarcity of pediatric cases and the current literature on the topic, as well as the absence of high-quality evidence-based guidelines, has led pediatricians to share experiences and personal communication via online meetings and open access medical education channels. The use of webinars and communication about newly released papers on social media channels such as Twitter, Telegram and WhatsApp, greatly improved the dissemination of knowledge among health care providers.

At the time of this review (7 May 2020), SARS-CoV2 has infected millions of people in the world and caused hundreds of thousands confirmed deaths, but data regarding the epidemiologic and clinical characteristics in neonates and children are still scarce. The purpose of this review was to evaluate the current literature that includes neonates and children to date, providing useful information for clinicians dealing with this selected population. The earliest epidemiologic data show that SARS-CoV2 has a dominant family-cluster transmission and that children present a mild form of COVID-19 (CFR: <0.1%), rarely requiring high-intensity medical treatment in PICU. Vertical transmission is unlikely, but not completely excluded. Diagnosis is performed primarily via molecular nucleic acid amplification testing. Patients with confirmed or suspected COVID-19 should be isolated and healthcare workers should wear appropriate protective equipment. Some clinical features (higher incidence of fever, vomiting and diarrhea, and a longer incubation period) are more common in children than in adults, as well as some radiologic aspects, including the presence of patchy shadow opacities on CT scan images. Treatment options are extrapolated from adult data. Thus, supportive and symptomatic treatments (oxygen therapy and antibiotics for bacterial coinfections) are recommended in these patients. More studies on neonates and children are needed to address these gaps and to provide more robust recommendations to manage COVID-19.

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Each author made a substantial contribution to this review and met the Pediatric Research authorship requirements. M.D.N., G.V.L., and A.L. contributed to the review design, data acquisition, and screening. M.D.N., M.A.B., and Y.G. contributed to the interpretation of the data and article drafting. M.D.N., F.L., and V.M.R. contributed to the article drafting and revisions. All authors have approved the final manuscript.

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Di Nardo, M., van Leeuwen, G., Loreti, A. et al. A literature review of 2019 novel coronavirus (SARS-CoV2) infection in neonates and children. Pediatr Res 89 , 1101–1108 (2021). https://doi.org/10.1038/s41390-020-1065-5

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covid 19 introduction for thesis

How to Write About Coronavirus in a College Essay

Students can share how they navigated life during the coronavirus pandemic in a full-length essay or an optional supplement.

Writing About COVID-19 in College Essays

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Experts say students should be honest and not limit themselves to merely their experiences with the pandemic.

The global impact of COVID-19, the disease caused by the novel coronavirus, means colleges and prospective students alike are in for an admissions cycle like no other. Both face unprecedented challenges and questions as they grapple with their respective futures amid the ongoing fallout of the pandemic.

Colleges must examine applicants without the aid of standardized test scores for many – a factor that prompted many schools to go test-optional for now . Even grades, a significant component of a college application, may be hard to interpret with some high schools adopting pass-fail classes last spring due to the pandemic. Major college admissions factors are suddenly skewed.

"I can't help but think other (admissions) factors are going to matter more," says Ethan Sawyer, founder of the College Essay Guy, a website that offers free and paid essay-writing resources.

College essays and letters of recommendation , Sawyer says, are likely to carry more weight than ever in this admissions cycle. And many essays will likely focus on how the pandemic shaped students' lives throughout an often tumultuous 2020.

But before writing a college essay focused on the coronavirus, students should explore whether it's the best topic for them.

Writing About COVID-19 for a College Application

Much of daily life has been colored by the coronavirus. Virtual learning is the norm at many colleges and high schools, many extracurriculars have vanished and social lives have stalled for students complying with measures to stop the spread of COVID-19.

"For some young people, the pandemic took away what they envisioned as their senior year," says Robert Alexander, dean of admissions, financial aid and enrollment management at the University of Rochester in New York. "Maybe that's a spot on a varsity athletic team or the lead role in the fall play. And it's OK for them to mourn what should have been and what they feel like they lost, but more important is how are they making the most of the opportunities they do have?"

That question, Alexander says, is what colleges want answered if students choose to address COVID-19 in their college essay.

But the question of whether a student should write about the coronavirus is tricky. The answer depends largely on the student.

"In general, I don't think students should write about COVID-19 in their main personal statement for their application," Robin Miller, master college admissions counselor at IvyWise, a college counseling company, wrote in an email.

"Certainly, there may be exceptions to this based on a student's individual experience, but since the personal essay is the main place in the application where the student can really allow their voice to be heard and share insight into who they are as an individual, there are likely many other topics they can choose to write about that are more distinctive and unique than COVID-19," Miller says.

Opinions among admissions experts vary on whether to write about the likely popular topic of the pandemic.

"If your essay communicates something positive, unique, and compelling about you in an interesting and eloquent way, go for it," Carolyn Pippen, principal college admissions counselor at IvyWise, wrote in an email. She adds that students shouldn't be dissuaded from writing about a topic merely because it's common, noting that "topics are bound to repeat, no matter how hard we try to avoid it."

Above all, she urges honesty.

"If your experience within the context of the pandemic has been truly unique, then write about that experience, and the standing out will take care of itself," Pippen says. "If your experience has been generally the same as most other students in your context, then trying to find a unique angle can easily cross the line into exploiting a tragedy, or at least appearing as though you have."

But focusing entirely on the pandemic can limit a student to a single story and narrow who they are in an application, Sawyer says. "There are so many wonderful possibilities for what you can say about yourself outside of your experience within the pandemic."

He notes that passions, strengths, career interests and personal identity are among the multitude of essay topic options available to applicants and encourages them to probe their values to help determine the topic that matters most to them – and write about it.

That doesn't mean the pandemic experience has to be ignored if applicants feel the need to write about it.

Writing About Coronavirus in Main and Supplemental Essays

Students can choose to write a full-length college essay on the coronavirus or summarize their experience in a shorter form.

To help students explain how the pandemic affected them, The Common App has added an optional section to address this topic. Applicants have 250 words to describe their pandemic experience and the personal and academic impact of COVID-19.

"That's not a trick question, and there's no right or wrong answer," Alexander says. Colleges want to know, he adds, how students navigated the pandemic, how they prioritized their time, what responsibilities they took on and what they learned along the way.

If students can distill all of the above information into 250 words, there's likely no need to write about it in a full-length college essay, experts say. And applicants whose lives were not heavily altered by the pandemic may even choose to skip the optional COVID-19 question.

"This space is best used to discuss hardship and/or significant challenges that the student and/or the student's family experienced as a result of COVID-19 and how they have responded to those difficulties," Miller notes. Using the section to acknowledge a lack of impact, she adds, "could be perceived as trite and lacking insight, despite the good intentions of the applicant."

To guard against this lack of awareness, Sawyer encourages students to tap someone they trust to review their writing , whether it's the 250-word Common App response or the full-length essay.

Experts tend to agree that the short-form approach to this as an essay topic works better, but there are exceptions. And if a student does have a coronavirus story that he or she feels must be told, Alexander encourages the writer to be authentic in the essay.

"My advice for an essay about COVID-19 is the same as my advice about an essay for any topic – and that is, don't write what you think we want to read or hear," Alexander says. "Write what really changed you and that story that now is yours and yours alone to tell."

Sawyer urges students to ask themselves, "What's the sentence that only I can write?" He also encourages students to remember that the pandemic is only a chapter of their lives and not the whole book.

Miller, who cautions against writing a full-length essay on the coronavirus, says that if students choose to do so they should have a conversation with their high school counselor about whether that's the right move. And if students choose to proceed with COVID-19 as a topic, she says they need to be clear, detailed and insightful about what they learned and how they adapted along the way.

"Approaching the essay in this manner will provide important balance while demonstrating personal growth and vulnerability," Miller says.

Pippen encourages students to remember that they are in an unprecedented time for college admissions.

"It is important to keep in mind with all of these (admission) factors that no colleges have ever had to consider them this way in the selection process, if at all," Pippen says. "They have had very little time to calibrate their evaluations of different application components within their offices, let alone across institutions. This means that colleges will all be handling the admissions process a little bit differently, and their approaches may even evolve over the course of the admissions cycle."

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Coronaviruses are a large family of viruses that are known to cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS).

A novel coronavirus (COVID-19) was identified in 2019 in Wuhan, China. This is a new coronavirus that has not been previously identified in humans.

This course provides a general introduction to COVID-19 and emerging respiratory viruses and is intended for public health professionals, incident managers and personnel working for the United Nations, international organizations and NGOs.

As the official disease name was established after material creation, any mention of nCoV refers to COVID-19, the infectious disease caused by the most recently discovered coronavirus.

Please note that the content of this course is currently being revised to reflect the most recent guidance. You can find updated information on certain COVID-19-related topics in the following courses: Vaccination: COVID-19 vaccines channel IPC measures: IPC for COVID-19 Antigen rapid diagnostic testing: 1) SARS-CoV-2 antigen rapid diagnostic testing ; 2) Key considerations for SARS-CoV-2 antigen RDT implementation

Please note: These materials were last updated on 16/12/2020.

Course contents

Emerging respiratory viruses, including covid-19: introduction:, module 1: introduction to emerging respiratory viruses, including covid-19:, module 2: detecting emerging respiratory viruses, including covid-19: surveillance:, module 3: detecting emerging respiratory viruses, including covid-19: laboratory investigations:, module 4: risk communication :, module 5 : community engagement:, module 6: preventing and responding to an emerging respiratory virus, including covid-19:, enroll me for this course, certificate requirements.

  • Gain a Record of Achievement by earning at least 80% of the maximum number of points from all graded assignments.


COVID-19 (2019 Novel Coronavirus) Research Guide

COVID-19 Research Guide Home

  • Research Articles Downloadable Database
  • COVID-19 Science Updates
  • Databases and Journals
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From the CDC’s COVID-19 (2019 Novel Coronavirus) website :

“COVID-19 (coronavirus disease 2019) is a disease caused by a virus named SARS-CoV-2. It can be very contagious and spreads quickly. Over one million people have died from COVID-19 in the United States.

COVID-19 most often causes respiratory symptoms that can feel much like a cold, the flu, or pneumonia. COVID-19 may attack more than your lungs and respiratory system. Other parts of your body may also be affected by the disease. Most people with COVID-19 have mild symptoms, but some people become severely ill.

Some people including those with minor or no symptoms will develop Post-COVID Conditions – also called “Long COVID.”

May 11, 2023, marks the end of the federal COVID-19 PHE declaration . After this date, CDC’s authorizations to collect certain types of public health data will expire.

The latest situation summary updates are available on CDC’s web page for COVID-19 . “

This guide provides resources for researching COVID-19. In this guide you can find the following:

  • The CDC Database of COVID-19 Research Articles became a collaboration with the WHO to create the WHO COVID-19 database during the pandemic to make it easier for results to be searched, downloaded, and used by researchers worldwide.
  • The last version of the CDC COVID-19 database was archived and remain available on this website.  Please note that it has stopped updating as of October 9, 2020 and all new articles were integrated into the WHO COVID-19 database .  The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.
  • COVID-19 Science Updates : To help inform CDC’s COVID-19 Response, as well as to help CDC staff stay up to date on the latest COVID-19 research, the Response’s Office of the Chief Medical Officer has collaborated with the CDC Office of Library Science to create a series called COVID-19 Science Update . This series, the first of its kind for a CDC emergency response, provides brief summaries of new COVID-19-related studies on many topics, including epidemiology, clinical treatment and management, laboratory science, and modeling. As of December 18, 2021, CDC has stopped production of the weekly COVID-19 Science Update.
  • Selected scholarly literature databases and journals available to help you find research about COVID-19.
  • Search alerts notify you when new research is published on COVID-19.
  • Search alerts available for Ovid , PubMed , Scopus , and News sources .
  • Selected sources for secondary data and statistics on COVID-19.
  • Selected websites and organizations where you can find more information on COVID-19.

Some resources within this guide are accessible only to those with a CDC user ID and password. Find a library near you that may be able to help you access similar resources by clicking the following links: https://www.worldcat.org/libraries  OR https://www.usa.gov/libraries .

Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings. HHS, PHS, and CDC assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by HHS, PHS, and CDC. Opinion, findings, and conclusions expressed by the original authors of items included in these materials, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of HHS, PHS, or CDC. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by HHS, PHS, or CDC.

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  • You will be subject to the destination website's privacy policy when you follow the link.
  • CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website.

Canada's Last Chance Lake Has Clues To Origin of Life On Earth: Study

The researchers were drawn towards the last chance lake after studying a 1990s unpublished master's thesis that had recorded unusually high levels of phosphate there..

Canada's Last Chance Lake Has Clues To Origin of Life On Earth: Study

Last Chance is a shallow water body, not more than one foot deep. (Representational Pic)

A lake in Canada holds clues to the origin of life on Earth, a new study has claimed. Some scientists claim that life emerged in volcanic landscapes, surrounded by a precise blend of chemicals and physical conditions. This was the likely setting around four billion years ago, and the study by David Catling and his colleagues offers new support for the idea. They studied the Last Chance Lake - a shallow, salty body of water situated on a volcanic plateau in Canada's British Columbia.

The researchers said that the lake holds clues that carbonate-rich lakes in ancient Earth could have been a "cradle of life". The study has been published in journal Nature on January 9.

"We were able to look for the specific conditions that people use to synthesise the building blocks of life in nature. We think that we have a very promising place for the origin of life," said Mr Catling, a University of Washington professor of geosciences.

They were drawn towards the lake after studying a 1990s unpublished master's thesis that had recorded unusually high levels of phosphate there.

Last Chance is a shallow water body, not more than one foot deep. It is located on a volcanic plateau over 1,000 metres above sea level. The lake has the highest levels of concentrated phosphate ever recorded in any natural body of water on Earth. It is more than 1,000 times more than what is typical for oceans or lakes, CNN quoted Sebastian Haas, a postdoctoral researcher studying the microbiology and chemistry of aquatic environments at the University of Washington as saying. He is the lead author of the paper.

Phosphate is a critical component of biological molecules and contains life-sustaining element phosphorous. It is found in molecules such as RNA and DNA.

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The water samples were collected from the lake between 2021 and 2022. An analysis revealed that apart from phosphate, the water also contained the mineral dolomite.

The compounding chemical processes, influenced by minerals from the volcanic rock that the lake formed upon, as well as an arid climate, effectively produced the unique concentrations of phosphate - a set of conditions that researchers believe could have once led to the emergence of life on Earth, according to Mr Haas.

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A literature review of the economics of COVID‐19

Abel brodeur.

1 Department of Economics, University of Ottawa, Ottawa Ontario, Canada

Suraiya Bhuiyan

Associated data.

Table B: COVID‐19 ‐ Timeline

Figure A: Cumulative COVID‐19 Cases and Deaths – Global Pandemic (as on 30 November 2020)

Table C: Cumulative Cases: Top 10 Countries (as of 30 November 2020)

The goal of this piece is to survey the developing and rapidly growing literature on the economic consequences of COVID‐19 and the governmental responses, and to synthetize the insights emerging from a very large number of studies. This survey: (i) provides an overview of the data sets and the techniques employed to measure social distancing and COVID‐19 cases and deaths; (ii) reviews the literature on the determinants of compliance with and the effectiveness of social distancing; (iii) mentions the macroeconomic and financial impacts including the modelling of plausible mechanisms; (iv) summarizes the literature on the socioeconomic consequences of COVID‐19, focusing on those aspects related to labor, health, gender, discrimination, and the environment; and (v) summarizes the literature on public policy responses.


The World was gripped by a pandemic over the first half of 2020, of which the second wave emerged in the Fall. It was identified as a new coronavirus (severe acute respiratory syndrome coronavirus 2, or SARS‐CoV‐2), and later renamed as Coronavirus Disease‐19 or COVID‐19 (Qiu et al., 2020 ). While COVID‐19 originated in the city of Wuhan in the Hubei province of China, it has spread rapidly across the World, resulting in a human tragedy and in tremendous economic damage. By the end of November 2020, there had been close to 63 million reported cases of COVID‐19 globally and over 1.4 million deaths.

Pandemics are anything but new, and they have had severe, adverse economic impacts in the past; COVID‐19 is not expected to be any different (see the Online Appendix for a brief history of past pandemics and their socioeconomic consequences). Given the rapid spread of COVID‐19, countries across the World have adopted several public health measures intended to prevent its spread, including social distancing (Fong et al., 2020 ). According to Mandavilli ( 2020 ), this strategy saved thousands of lives, both during other pandemics, such as the Spanish flu of 1918, and more recently a flu outbreak that occurred in Mexico City in 2009. As part of social distancing measures, businesses, schools, community centers, and nongovernmental organization (NGOs) were required to close down, mass gatherings have been prohibited, and lockdown measures have been imposed in many countries, allowing travel only for essential needs. 1 The goal of these measures is to facilitate a “flattening the curve,” that is, a reduction in the number of new daily cases of COVID‐19 in order to halt their exponential growth and, hence, reduce pressure on medical services (John Hopkins University, 2020 ).

The spread of COVID‐19 has resulted in a considerable slowdown in economic activities. According to an early forecast of The World Bank ( 2020 ), global GDP in 2020 relative to 2019 is forecasted to fall by 5.2%. Similarly, the OECD ( 2020 ) forecasts a fall in global GDP by 6 to 7.6%, depending on whether or not a second wave of COVID‐19 emerges. In its latest forecast, the International Monetary Fund ( 2020 ) projected a contraction of 4.4% in light of the stronger than expected recoveries in advanced economies which lifted lockdowns during May and June of 2020. This was mainly the result of the unprecedented fiscal, monetary, and regulatory responses in these countries that helped to maintain household disposable income, protect cash flows for firms, and support credit provisions.

The economic implications will be wide ranging and uncertain, with different effects expected on labor markets, production supply chains, financial markets, and GDP levels. The negative effects may vary by the stringency of the social distancing measures (e.g., lockdowns and related restrictions), their length of implementation, and the degree of compliance with them. In addition, the pandemic and the subsequent interventions may well lead to higher levels of mental health distress, increased economic inequality, and particularly harsh effects on certain socio‐demographic groups.

The goal of this piece is to survey the emerging and already vast literature on the economic consequences of COVID‐19, and to synthesize the insights contained in a growing number of studies. Figure  1 illustrates the number of National Bureau of Economic Research (NBER) working articles that have been released related to the pandemic between March and November of 2020. 2 By the end of November 2020, there had been 247 articles related to COVID‐19. Similarly, 204 discussion articles on the pandemic were released by the IZA Institute of Labor Economics (IZA) from March to November of 2020. 3

An external file that holds a picture, illustration, etc.
Object name is JOES-35-1007-g001.jpg

COVID‐19 publications in 2020 in the NBER working paper series. [Color figure can be viewed at wileyonlinelibrary.com ]

Source : Authors’ compilation drawn from the NBER website

This article will focus on five broad areas: (i) the measurement of the spread of COVID‐19 and social distancing activities, (ii) the effectiveness and compliance with social distancing regulations, (iii) the economic impacts of COVID‐19 and the mechanisms giving rise to them, (iv) the socioeconomic consequences of lockdowns, and (v) the policy measures and regulations that have been implemented in response to the pandemic. One topic that we do not cover explicitly is the interface between COVID‐19 and financial markets. This omission is due partly to space constraints, but also to the fact that the outcomes in financial markets that are related to COVID‐19 are extremely volatile, and therefore, any analysis contained in our survey would be ephemeral.

The rest of the article is structured as follows. Section  2 provides an outline of the measurement of COVID‐19 spread and of social distancing actions by documenting and describing the most popular data sources. Section  3 discusses the socioeconomic determinants and the effectiveness of social distancing activities. Section  4 focuses on the economic and financial impacts including modelling of the plausible behavioral mechanisms. Section  5 reviews the literature on the socioeconomic consequences of social distancing measures, focusing on the labor‐related, health‐related, gender‐related, discriminatory, and environmental aspects. Section  6 consists of a summary of the economic impact of the policy responses. Section  7 provides the conclusion.


2.1. measurement of covid‐19 spread.

Before reviewing the potential economic impact and socioeconomic consequences, it is important to contextualize the data related to COVID‐19, without which it would not be possible to assess the scope of the pandemic. Timely and reliable data inform us of how and where the disease is spreading, what impact the pandemic has on the lives of people around the World, and to what extent the counter measures that are taken are successful (Roser et al., 2020 ).

Four key indicators are: (i) the total number of tests carried out, (ii) the number of confirmed COVID‐19 cases, (iii) the number of confirmed COVID‐19 deaths, and (iv) the number of people who have recovered from COVID‐19. These numbers are provided by different local, regional, and national health agencies/ministries across countries. However, for research and educational purposes, the data are accumulated by the Center for Systems Science and Engineering at Johns Hopkins University. 4 The database provides the figures as well as visual maps of the distribution of cases across the World. They are reported at the provincial level for China, at the city level for the United States of America, Australia, and Canada, and at the country level for all other countries (Dong et al., 2020 ). The data are corroborated with the WHO, 5 the Center for Disease Control (CDC) in the United States, and the European Center for Disease Control (ECDC).

Based on these figures, the Case Fatality Rate (CFR) is calculated as the number of confirmed deaths divided by the number of confirmed cases, which gives the mortality rate. 6 However, Roser et al. ( 2020 ) caution against taking the CFR numbers at face value to assess mortality risks, 7 because the CFR is based on the number of confirmed cases. Due to limited and sporadic testing capacities, not all COVID‐19 cases can be confirmed. Moreover, the CFR reflects the incidence of the disease in a particular context at a particular point in time. Therefore, CFRs are subject to changes over time and are sensitive to the location and population characteristics.

Recent studies indicate that there are large measurement errors associated with COVID‐19 case numbers. Using data on influenza‐like illnesses (ILI) from the CDC, Silverman et al. ( 2020 ) show that ILIs can be a useful predictor of COVID‐19 cases in the United States. The authors find that there was an escalation in the number of ILI patients during March of 2020. These cases could not be properly identified as COVID‐19 cases due to the lack of testing capabilities during the early stages of the pandemic's progression. The authors suggest that the surge in ILIs may have corresponded to 8.7 million new COVID‐19 cases between March 8 and March 28, most of which were probably not diagnosed. Based on imputation, that figure suggests that almost 80% of all actual cases in the United States during that time period were never diagnosed.

While the dataset mentioned above focuses on counts and tests, the COVID Tracking Project 8 in the United States provides additional data on patients who have been hospitalized, are in intensive care units (ICUs), and are on ventilator support for each of the 50 states. It also grades each state on data quality. Recently, it has included the COVID Racial Data Tracker , 9 which shows the race and the ethnicity of individuals affected by COVID‐19. All of these combined measures and statistics provide a more comprehensive perspective of the spread of the pandemic in the United States.

2.2. Measurement of social distancing

Compared to measuring the spread of the virus, social distancing is not easy to quantify. We determined from the literature that there are three main techniques that are employed: (i) developing and calculating measures of the mobility of the population, (ii) modelling proxies, and (iii) calculating indices. Proxies and indices are based on data related to the observed spread of infection and to the implementation of social distancing policies, respectively. On the other hand, the movements of people are based on their observed travelling patterns. Mobility measures have been used extensively in recent months to discern mobility patterns during the pandemic (Nguyen et al., 2020 ). However, mobility data providers have slight differences in their methodologies. Table  1 provides a summary of how different mobility data providers compile their data.

Social distancing— Mobility measures and how they work

Mobility data are more dynamic and are available at a daily frequency. They can also be used to measure the effect of social distancing on other variables, such as adherence to shelter‐in‐place policies or labor employment patterns (Gupta et al., 2020 ). They also offer key insights into human behavior. For example, “Safegraph” data suggest that social activity in the United States started declining substantially and rapidly well before lockdown measures were imposed (Farboodi et al., 2020 ).

Outside of the United States, a large number of studies have relied on Google LLC Community Mobility Reports. For China, mobility has been mostly measured using data from Baidu Inc. For example, Kraemer et al. ( 2020 ) document how COVID‐19 spread in China using Baidu Inc. data. They investigate travel history from Wuhan to other cities in China, finding that the spatial distribution of cases in other cities was correlated with individual peoples’ travel histories. However, after the implementation of social distancing measures in these cities, the correlation no longer held. Therefore, the authors conclude that local lockdowns rather than travel restrictions helped to mitigate the spread and transmission of COVID‐19 in cities outside Wuhan. See Coelho et al. ( 2020 ) for an examination of the spread of COVID‐19 in Brazil using daily air travel statistics from the Official Airline Guide to measure mobility.

Mobility data do have their own limitations and are not frequently used in the case of epidemics, even though they might be useful (Oliver et al., 2020 ). Mobility data are a proxy for time spent in different locations. They do not allow one to determine the situational context of the contacts that are reported, which are needed to understand the spread of COVID‐19, that is whether they occur in the workplace or in the general community (Martín‐Calvo et al., 2020 ). Those two situations involve different levels of the inherent risk of transmission. In regards to the productive activities of the individuals that are tracked, information on the context is also indeterminate. For those who are working virtually from their homes, for instance, these measures do not capture the value‐added stemming from the time that they allocate to their jobs in the labor market. It is also likely that the quality of these measures can deteriorate when overall unemployment rates and job disruptions are high (Gupta et al., 2020 ). 10 Telecom operator data are deemed to be more representative than locational data, as the former are not limited to people with smartphones, GPS locators, and histories of travel using GPS location (Lomas, 2020 ).

Social media has also been used to measure mobility patterns. Galeazzi et al. (2020) analyze the effect of lockdowns in France, Italy, and United Kingdom on national mobility patterns by exploiting geolocalized data observed from 13 million Facebook users. The authors predictably find that people transition toward localized, short‐ range mobility patterns instead of international, long‐range patterns. However, mobility patterns display heterogeneity across countries. In France and the United Kingdom, mobility is more “concentrated” around huge, central metropolises that are largely disconnected from the provinces, which helps to reduce transmission of the virus. In Italy, on the other hand, the population is more “distributed” across clusters around four major cities that remain interconnected, thus permitting persistent spread.


A large range of social distancing policies have been implemented, ranging from full‐scale lockdowns to voluntary self‐compliance measures. 11 For example, Sweden imposed relatively light restrictions (Juranek & Zoutman, 2020 ). Large‐scale events were prohibited, and restaurants and bars were restricted to table service only; however, private businesses were generally allowed to operate freely. The population was encouraged to stay at home if they were feeling unwell and to limit social interactions if possible (T.M. Andersen et al., 2020 bib18 ).

People tend to adopt social distancing practices when there is a specific incentive to do so in terms of risk to health and financial cost (Makris, 2020 ). Maloney and Taskin ( 2020 ) attribute voluntary, cooperative actions to either fear of infection or to a sense of social responsibility. Stringent social distancing measures tend to be implemented in countries with a greater proportion of elderly residents, a higher population density, a greater proportion of employees working in vulnerable occupations, higher degrees of democratic freedom, a higher incidence of international travel, and greater distances from the Equator (e.g., Jinjarak et al., 2020 ). Appealing to a game theoretic approach, Cui et al. ( 2020 ) argue that states sharing economic ties will be “tipped” to reach a Nash equilibrium, whereby all other states comply with shelter‐in‐place policies. 12

Social distancing policy determinants have been linked to political party characteristics, political beliefs, and partisan differences (Baccini & Brodeur, 2021 ; Barrios & Hochberg, forthcoming; Murray & Murray, 2020 ). Barrios and Hochberg (forthcoming) correlate the risk perception for contracting COVID‐19 with partisan differences. They find that, in the absence of the imposition of social distancing, counties in the United States which had higher vote shares for Donald Trump are less likely to engage in social distancing. This persists even when mandatory stay‐at‐home measures are implemented across states. Allcott et al. ( 2020 ) find a similar pattern. In addition, the authors show through surveys that Democratic and Republican supporters have different risk perceptions about contracting COVID‐19, and hence hold divergent views regarding the importance of following social distancing measures. These stylized facts make it hard to estimate the causal effect of COVID‐19 on electoral outcomes (Baccini et al., 2021 ).

Researchers are trying to determine the effectiveness of social distancing policies in reducing social interactions and ultimately infections and deaths. Abouk and Heydari ( 2021 ) show that reductions in outside‐the‐home social interactions in the United States are driven by a combination of governmental regulations and voluntary measures, with a strong causal impact for the implementation of statewide stay‐at‐home orders, but more moderate impacts for nonessential business closures and limitations placed on bars/restaurants. Ferguson et al. ( 2020 ) argue that multiple interventions are required in order to have a substantial desired impact on transmission. The optimal mitigation strategy, which is a combination of case isolations, home quarantining, and social distancing of high‐risk groups, would reduce the number of deaths by half and the demand for beds in ICUs by two‐thirds in the United Kingdom and the United States.

Some studies focus on the impact of social distancing on COVID‐19 cases, hospitalizations, etc. For example, Fang et al. ( 2020 ) argue that if lockdown policies had not been imposed in Wuhan, then the infection rates would have been 65% higher in cities outside of Wuhan. Hartl et al. ( 2020 ) show that growth rate of COVID‐19 cases in Germany dropped from 26.7 to 13.8% within 7 days after implementation of lockdowns in the country. Greenstone and Nigam ( 2020 ) project that 3 to 4 months of adherence to social distancing regulations would reduce the number of cases in the United States by 1.7 million by October of 2021, 630,000 of which would translate into averted overcrowding of ICUs in hospitals. Friedson et al. ( 2020 ) argue that early intervention in California helped to reduce significantly the numbers of COVID‐19 cases and deaths during the first 3 weeks following its enactment. Note that this set of interventions falls well short of an economic shutdown.

Similarly, Dave, Friedson, Matsuzawa, Sabia, et al. ( 2020 ) find that counties in Texas that adopted shelter‐in‐place orders earlier than the statewide shelter‐in‐place order experienced a 19 to 26% fall in the rates of COVID‐19 case growth 2 weeks after implementation of such orders. M. Andersen et al. ( 2020 ) find that temporary paid sick leave, a federal mandate enacted in the United States, which allowed private and public employees 2 weeks of paid leave, led to increased compliance with stay‐at‐home orders. On a more global scale, Hsiang et al. ( 2020 ) show that social distancing interventions prevented or delayed around 62 million confirmed cases, corresponding to the aversion of roughly 530 million total infections in China, South Korea, Italy, Iran, France, and the United States within 7 days.

Another important related issue is the determinants of compliance behavior (e.g., Coelho et al., 2020 ; Fan et al., 2020 ). The documented socioeconomic determinants of the degree of compliance with social distancing (lockdowns or safer‐at‐home orders) include, among other factors, income level, trust, and social capital, public discourse, and to some extent, news channel viewership. The degree of ethnic diversity is another documented socioeconomic determinant of social distancing (Egorov et al., 2021 ). Galasso et al. ( 2020 ) rely on survey data from eight OECD countries and provide evidence that women are more likely than men to agree with restrictive public policy measures and to comply with them. Chiou and Tucker ( 2020 ) show that Americans living in higher‐income regions with access to high‐speed internet are more likely to comply with social distancing directives. Coven and Gupta ( 2020 ) find that residents of low‐income neighborhoods in New York City comply less with shelter‐in‐place activities during non‐working hours. According to the authors, this pattern is consistent with the fact that low‐income populations are more likely to be front line, “essential” workers and are also are more likely to make frequent retail shopping visits for essentials, making for two compounded effects. People with lower income levels, less flexible work arrangements (e.g., the inability to work remotely), and a lack of accessible interior space outside of bedrooms are less likely to engage in social distancing (Papageorge et al., 2020 ). Last, Bonaccorsi et al. ( 2020 ) analyze the heterogeneous impacts of lockdowns by socioeconomic conditions of people in Italy. Using mobility data from Facebook, they provide evidence that mobility reduction is higher in municipalities which have stronger fiscal capacity and also those which have lower per‐capita income levels. The authors conclude that the pandemic has disproportionately affected poor individuals within municipalities with strong fiscal capacity in Italy.

Individual beliefs and social preferences should also be taken into consideration, as they affect behavior and compliance. Based on an experimental setup with participants in the United States and the United Kingdom, Akesson et al. ( 2020 ) conclude that individuals overestimated the infectiousness of COVID‐19 relative to expert suggestions. If they were exposed to expert opinion, individuals were prone to correct their beliefs. However, the more infectious COVID‐19 was deemed to be, the less likely they were to undertake social distancing measures. This was perhaps due to the belief that the individual will contract COVID‐19 regardless of his/her social distancing practices. Briscese et al. ( 2020 ) model the impact of “lockdown extension” on compliance using a representative sample of residents from Italy. The authors find that if a given hypothetical extension is shorter than expected (i.e., a positive surprise), the residents are more willing to engage in self‐isolation. Therefore, to ensure compliance, these authors suggest that it is imperative for the government or local authorities to work on communication and to manage peoples’ expectations. Campos‐Mercade et al. ( 2021 ) examine the relationship between social preferences and social distancing compliance. The authors find that people who exhibit prosocial behavior (in this instance individuals who claim that they do not want to expose others to risks) are more likely to follow social distancing measures and other health‐related guidelines.

Bargain and Aminjonov ( 2020 ) demonstrate that residents in European regions with high levels of trust decrease their mobility related to non‐necessary activities compared to regions with lower levels of trust. Brück et al. ( 2020 ) document a negative relationship between being in contact with sick people and trust in people and institutions. Similarly, Brodeur et al. ( 2020 ) find that counties in the United States exhibiting relatively more trust in others decrease their mobility significantly once a lockdown policy is implemented. They also provide evidence that the estimated effect on postlockdown compliance is especially large if people tend to place trust in the media, and relatively smaller if they tend to trust in science, medicine, or government.

Researchers also think about this chain of causality in reverse. Aksoy Eichengreen, and Saka ( 2020 ) find that individuals’ degrees of exposure to epidemics (especially during the ages 18 to 25) has a negative effect on their confidence in political institutions. These individuals are also less likely to have confidence in their health care systems during the times of pandemics. Barrios et al. ( 2021 ) and Durante et al. ( 2021 ) provide evidence that regions with stronger civic culture engaged in more voluntary social distancing. Aksoy, Ganslmeier, and Poutvaara ( 2020 ) find that a high level of public attention (measured through the share of Google shares containing matters related to COVID‐19) has a significant correlation with the timing of implementation of social distancing measures. This relationship is mostly applicable for countries with high quality of institutions. Last, Bartscher et al. ( 2020 ) show that higher levels of social capital (proxied through voter turnout in parliamentary elections) lead to fewer cases per capita accumulated from mid‐March to mid‐May in selected European countries and United Kingdom.

Daniele et al. ( 2020 ) investigate the effect of the COVID‐19 shock on sociopolitical attitudes as opposed to the impact of latter on the spread of the virus. Employing a randomized survey flow design for 8,000 respondents in Germany, Italy, Netherlands and Spain, the authors find that COVID‐19 has led to a deterioration in the levels of interpersonal and institutional trust. It has also lowered support for the European Union in general and for social welfare spending financed by taxes. The authors conclude that these results are driven by the “economic insecurity” rather than the “health” dimensions resulting from the crisis.

Simonov et al. ( 2020 ) analyze the causal effect of cable news viewership on social distancing compliance. The authors examine the average partial effect of Fox News viewership, a news channel that has mostly refuted expert recommendations from leaders of the United States and global public health communities on the severity of COVID‐19 and on compliance, and find that a 1 percentage point increase in Fox News viewership reduced the propensity to stay at home by 8.9 percentage points. Bursztyn et al. ( 2020 ) show that greater exposure to the Hannity show compared to the Tucker Carlson Tonight show in Fox News is associated with larger COVID‐19 case numbers and deaths. This is because the former TV host downplayed the importance of COVID‐19, while the latter provided a serious warning on the same topic during early February. The variation between the messages in the two shows led to changes in behavior in response to COVID‐19.

Table  2 provides a summary of the literature related to the determinants (i.e., factors which influence implementation of social distancing as a policy measure), compliance with social distancing (i.e., whether people are actually following social distancing measures), and their effectiveness (i.e., evidence of success in reducing COVID‐19 cases).

Determinants, compliance and effectiveness of social distancing measures: Summary of studies


4.1. plausible mechanisms for macroeconomic impact.

To understand the potential negative economic impact of COVID‐19, it is important to comprehend the economic transmission channels through which the shocks will adversely affect the economy. According to Carlsson‐Szlezak et al. ( 2020a , b ), there are three main transmission channels. The first is the direct impact, which is related to reduced consumption of goods and services. Prolonged lengths of the pandemic and the concomitant social distancing measures might reduce consumer confidence by keeping consumers at home, wary of discretionary spending, and pessimistic about their long‐term economic prospects. The second one is the indirect impact working through financial market shocks and their effects on the real economy. Household wealth will likely fall, savings will increase, and consumption spending will decrease further. The third consists of supply‐side disruptions; as restrictions halt or hamper production activities, they will negatively impact supply chains, labor demand, and employment, leading to prolonged periods of lay‐offs and rising unemployment. In particular, Baldwin ( 2020 ) discusses the expectation shock by which a “wait‐and‐see” attitude is adopted by economic agents. The author argues that this is common during economic climates characterized by uncertainties, as there is less confidence in markets and in engaging in economic transactions. Ultimately, the intensity of the shock is determined by the underlying epidemiological properties of the virus, consumer behaviour, and firm behavior in the face of adversity and uncertainty, and public policy responses. To understand the implications of the spread of the virus and the consequent social distancing measures on economic activities, a number of researchers have integrated canonical epidemiology models such as the susceptible, infected, resolved model (SIR) with macroeconomic models (see the Online Appendix for a detailed review of these models).

Gourinchas ( 2020 , p. 33) summarizes the effect on the economy by stating: “A modern economy is a complex web of interconnected parties: employees, firms, suppliers, consumers, and financial intermediaries. Everyone is someone else's employee, customer, lender, etc.” Due to the very high degrees of interconnectiveness and specialization of productive activities, a breakdown in the supply chains and the circular flows will have cascading effects. Baldwin ( 2020 ) describes the impact of COVID‐19 and subsequent social distancing measures on the macroeconomy within a circular flow framework.

It is also important to understand the processes that generate recoveries from economic crises. Carlsson‐Szlezak et al. ( 2020a ) explain different types of recovery in the aftermath of negative shocks through the concept of “shock geometry.” There are three broad scenarios of economic recoveries, which we mention in ascending order of their severity. First, there is the most optimistic one labelled “V‐shaped,” whereby aggregate output is displaced and quickly recovers to its pre‐crisis path. Second, there is the “U‐shaped” path, whereby output drops swiftly but does not return swiftly to its precrisis path. The gap between the former trajectory of output and the actual one remains large for quite some time, but recovery eventually occurs. Third, in the case of the very grim “L‐shaped” path, output drops and reaches a trough, but subsequent growth rates remain very low. The gap between the former and the new output paths continues to widen. Another scenario of economic recovery often mentioned is the “K‐shaped” one, which occurs when, following a recession, different parts of the economy recover at different rates, times, or magnitudes.

Carlsson‐Szlezak et al. ( 2020b ) state that after previous pandemics, such as the 1918 Spanish Influenza, the 1958 Asian Influenza, the 1968 Hong Kong influenza, and the 2002 SARS outbreak, economies have tended to experience “V‐shaped” recoveries. However, the pattern for the COVID‐19 economic recovery is not expected to be straightforward. This is because the effects on employment due to social distancing measures and lockdowns are expected to be much larger. According to Gourinchas ( 2020 ), during a short period, as much as 50% of the working population might not be able to find work. Moreover, even if no containment measures are implemented, a recession would occur anyway, fueled by the precautionary and/or risk‐averse behavior of households and firms faced with the uncertainty of dealing with a pandemic as well as with an inadequate public health response (Gourinchas, 2020 ).

Guerrieri et al. ( 2020 ) show that in a multisector model with certain assumptions, such as incomplete markets, low substitutability across sectors, and liquidity‐constrained consumers, COVID‐19 imparts a supply shock which works through lockdowns, layoffs, firm closures, etc. The subsequent impact would be a drop in aggregate demand and a demand‐deficient recession, that is, a “Keynesian supply shock.”

Baqaee and Farhi ( 2020 ) analyze the impact through a disaggregated Keynesian model comprised of multiple sectors, factors of production, and input‐output linkages with different features, such as nominal wage rigidities and credit constraints. They find that negative supply shocks are stagflationary, whereas negative demand shocks are deflationary. The policy implications are somewhat ambiguous. Policies that boost aggregate supply (e.g., providing subsidies to businesses, relaxing lockdowns, etc.) might not be effective in increasing demand in certain demand‐constrained sectors. Similarly, demand‐inducing policies (e.g., lower interest rates, more generous social insurance, etc.) might lead to supply shortages and inflationary pressures in certain sectors.

4.2. Quantitative macroeconomic impacts

As the pandemic unfolds, many researchers have been thinking about the economic impact from a historical perspective. Ludvigson et al. ( 2020 ) try to quantify the macroeconomic impact of costly disasters (natural and manmade) and translate them into estimates of the impact of COVID‐19. They find that in a fairly conservative scenario, pandemics, such as COVID‐19, are tantamount to large, multiple‐period exogenous shocks. Using a “costly disaster” index, the authors find that COVID‐19 is constituted of multi‐period shocks in the United States, which leads to a 12.75% drop in industrial production, a 17% loss in service employment, sustained and drastic reductions in air travel, and macroeconomic uncertainties which linger for up to 5 months. Jordà et al. ( 2020 ) analyze the rate of return on the real natural interest rate (the level of real returns on safe assets resulting from the demand and supply of investment capital in a noninflationary environment) from the 14 th century to 2018. Theoretically, a pandemic is supposed to induce a downward negative shock to the real natural interest rate. This is because investment demand decreases due to excess capital per labor unit (i.e., a scarcity of labor being utilized), while savings flows increase due to either precautionary reasons or to replace lost wealth. The authors find that the natural rate of interest may be about 2 percentage points lower than it would otherwise have been some 20 years after the pandemic, and only return to counterfactual levels after 40 years.

Analysis based on historical data, however, might not be relevant in this case. According to Baker et al. ( 2020 ), COVID‐19 has led to massive spikes in uncertainty, and there are no close historical parallels. Because of the speed of evolution and timely requirements of data, the authors suggest that one should utilize forward‐looking uncertainty measures to ascertain its impact on the economy. They formulate the uncertainty measure from the Standard & Poor's 500 Volatility Index (VIX) and the news‐based economic policy uncertainty (EPU) index developed by Baker et al. ( 2016 ). Using a real business cycle model, the authors find that a COVID‐19 shock leads to a year‐over‐year contraction of GDP by 11% in fourth quarter of 2020. According to the authors, more than half of the contraction is caused by COVID‐19‐induced uncertainty. Based on a similar approach, Altig et al. ( 2020 ) conduct an analysis of different forward‐looking uncertainty measures during the pandemic. Coibion et al. ( 2020a ) use surveys to assess the macroeconomic expectations of households in the United States. They find that it is primarily lockdowns, rather than the infections themselves, that lead to declines in consumption spending and employment, lower inflationary expectations, increased uncertainty, and lower mortgage payments being made.

Eichenbaum et al. ( 2020 ) model the interactions between economic decisions and the spread of the virus. They find that, without any mitigation measures, aggregate consumption falls by 9.3% over a 32‐week period. On the other hand, labor supply or hours worked follow a U‐shaped pattern, with a peak decline of 8.25% in the 32nd week from the start of the pandemic. These reductions decrease peak infection rates and death tolls from 7% and 0.30% to 5% and 0.26% respectively, but worsen the magnitude of the recession. Infected people fail to internalize the impact of their choices on the spread of the virus. Therefore, the optimal containment policy increases the severity of the recession but saves lives. 13

Mulligan ( 2020 ) assesses the opportunity cost of “shutdowns” in order to document the macroeconomic impact of COVID‐19. Within the National Accounting Framework for the United States, the author extrapolates the welfare loss stemming from “nonworking days,” the fall in the labor‐capital ratio resulting from the absence/layoff of workers, and the resulting idle capacity of workplaces. After accounting for dead‐weight losses stemming from fiscal stimulus, the replacement of normal import and export flows with black market activities, and the effect on nonmarket activities (lost productivity, missed schooling for children and young adults), the author finds the welfare loss to be approximately $7 trillion per year of shutdown. Medical innovations, such as vaccine development, contact tracing, and workplace risk mitigation can help to offset the welfare loss by around $2 trillion per year of shutdown.

Other researchers have examined the supply side. Bonadio et al. ( 2020 ) use a quantitative framework to simulate a global lockdown as a contraction in labor supply for 64 countries. The authors find that the average decline in real GDP constitutes a major contraction in economic activity, with a large share attributed to disruptions in global supply chains. Elenev et al. ( 2020 ) model the impact of COVID‐19 as a fall in worker productivity and as a decline in labor supply, which both adversely affect firm revenue. The fall in revenue and the subsequent non‐repayment of debt‐servicing obligations spur a wave of corporate defaults, which might also bring down financial intermediaries. Céspedes et al. ( 2020 ) formulate a minimalist economic model in which the virus also leads to losses in productivity. The authors predict a vicious cycle triggered by the loss of productivity causing lower collateral values, in turn limiting the amount of borrowing activity, subsequently leading to decreased employment, followed by a further decline in productivity. The shock is thus magnified through an “unemployment and asset price deflation doom loop” (see Fornaro & Wolf, 2020 ).

Consumption pattern responses and debt responses from pandemic shocks had not been analyzed prior to COVID‐19 (Baker et al., 2020 ). Using transaction‐level household data, these authors find that households sharply increased their spending during the initial period in specific sectors such as retail and food spending. These increases, however, were followed by a decrease in overall spending. Similarly, Chang and Meyerhoefer ( 2020 ) show that consumers in Taiwan have increased food purchases from online platforms. Binder ( 2020 ) conduct an online survey of 500 United States consumers to investigate their concerns and responses related to COVID‐19, which indicated those items of consumption on which they were spending either more or less. They find that 28% of the respondents in that survey postponed future travel plans, and that 40% forewent food purchases. Interestingly, Binder ( 2020 ) finds from the surveys that consumers tend to associate graver concerns about COVID‐19 with higher inflationary expectations, a sentiment which serves as a proxy for “pessimism” or “bad times.”

Clemens and Veuger ( 2020 ) focus on the declines in government sales and income tax collections across US states. According to the authors, COVID‐19 has led to a substantial decline in consumption levels compared to income levels. This pattern is unlike the case in previous recessions, during which income decreased more than consumption. The authors find that the COVID‐19 pandemic will reduce the states’ tax collections by $42 billion in the second quarter of 2020. For fiscal year 2021, the authors anticipate an overall decline in sales and income tax revenues of $106 billion with heterogenous losses across US states.

McKibbin and Fernando ( 2020 ) estimate the aggregate economic costs. Using a hybrid DSGE/CGE global model, the authors model COVID‐19 as a negative shock to labor supply, consumption spending, financial markets, but as a positive shock to government expenditure, particularly stemming from health‐related expenditures. The authors outline seven different scenarios and provide a range of estimates of the increase in mortality and the fall in GDP for a number of countries across the world. In the case of the most contained outbreak, the number of deaths reaches around 15 million, while the reduction in global GDP is around $2.4 trillion in 2020.

Eppinger et al. ( 2020 ) use a quantitative international trade model with input‐output linkages for 43 countries to assess the impact of COVID‐19 supply shock on global value chains. They find that due to the supply shock, China experienced a welfare loss of 30% with moderate (positive or negative) spillover to other countries. Estimating a simulation consisting of a counterfactual scenario described as “without global value chains,” the authors find that welfare losses are reduced for some countries by as much as 40%, while they are magnified for others.

The economic impact of shocks, such as pandemics, is usually measured with aggregate time series data. However, these datasets are available only after a certain lag. In order to analyze the economic impact at a higher frequency, Lewis et al. ( 2020 ) developed a weekly economic index (WEI) using 10 different economic variables to track the economic impact of COVID‐19 in the United States. These authors report that between March 21 and March 28, the WEI declined by 6.19%. This was driven by a decline in consumer confidence, a fall in fuel sales, a rise in unemployment insurance (UI) claims, and changes in other variables. Similarly, Demirguc‐Kunt et al. ( 2020 ) estimate the economic impact of social distancing measures via three high‐frequency proxies (electricity consumption, nitrogen dioxide emissions, and mobility records). The authors find that social distancing measures led to a 10% decline in economic activity (as measured by electricity usage and emissions) across European and Central Asian countries between January and April. Chetty et al. ( 2020 ) develop a real‐time economic tracker using daily statistics on consumption, employment, business revenue, job postings, and other variables. The authors show that the initial slowdown in economic activity was partly driven by reductions in consumption by high‐income individuals. These spending shocks negatively affected business revenues catering to high‐income individuals. Subsequently, low‐income individuals working for these businesses lose much of their incomes and reduce their consumption levels. Kapteyn et al. ( 2020 ) tracked a representative sample of 7,000 respondents in Los Angeles County, California every 2 weeks to assess the impact of COVID‐19 over time.

Brinca et al. ( 2020 ) estimate the labor demand and supply shocks occurring in different sectors in the US economy employing a Bayesian structural vector autoregression model. They find that the decrease in work hours can be attributed to negative labor supply shock, a result that they suggest has important policy design implications. A negative labor supply shock is directly related to the lockdown and might be mitigated once such policies are lifted.


We now review studies documenting the socioeconomic consequences of COVID‐19 and the ensuing lockdowns. Social distancing and lockdown measures have been shown to adversely affect labor markets, mental health and well‐being, racial inequality, and gender‐related outcomes. The environmental implications, while likely to be positive overall, also deserve careful analysis.

5.1. Labor market outcomes

A large number of studies document the effects on the variables of hours of work and job losses (e.g., Kahn et al., 2020 ). The major increases in unemployment observed in the United States are driven partly by lockdowns and social distancing policies (Rojas et al., 2020 ). Accounting for cross‐state variation in the timing of business closures and stay‐at‐home mandates in the United States, Gupta et al. ( 2020 ) find that the employment rate in the United States falls by about 1.7 percentage points for every extra 10 days that a state experienced a stay‐at‐home mandate during the period of March 12th to April 12th.

Coibion et al. ( 2020b ) find that the level of unemployment and job losses in the United States is more severe than one might judge based on the rise in UI claims, which is to be expected given the low coverage rate of the UI regimes in the United States. They also project a severe fall in the labor participation rate in the long run accompanied by an increase in the number of “discouraged workers” (jobless workers who have stopped actively searching for work, effectively withdrawing from the labor force). This phenomenon might be due to the disproportionate impact of COVID‐19 on the older population. Aum et al. ( 2020a , 2021b ) find that an increase in infections leads to a drop in local employment even in the absence of lockdowns in South Korea, whose government did not mandate them. This estimated impact was higher for countries, such as the United States and the United Kingdom, where mandatory lockdown measures were imposed.

Adams‐Prassl et al. ( 2020a ) analyze the inequality of the distributions of job and income losses based on the type of job held and on individual characteristics for the United States and the United Kingdom. The authors find that workers who can perform none of their employment tasks from home are more likely to lose their job. This study also finds that younger individuals and people without a university education were significantly more likely to experience drops in their income. Yasenov ( 2020 ) finds that workers with lower levels of education, younger adults, and immigrants are concentrated in occupations whose tasks are less likely to be performed from home. Similarly, Alstadsæter et al. ( 2020 ) find that the pandemic shock in Norway has a strong socioeconomic gradient, as it has disproportionately affected the financially vulnerable population, including parents with younger children.

Béland, Brodeur, and Wright ( 2020 ) discuss heterogeneous effects across occupations and workers in the United States, showing that occupations that have a higher share of workers working remotely were less affected by COVID‐19. On the other hand, occupations with relatively more workers working in proximity to others were more affected. They also find that occupations classified as “more exposed to disease” are less affected, which is possibly due to the number of essential workers in these occupations. Based on these results, it can be reasonably expected that workers might change (or students might select different) occupations in the medium term. Bui et al. ( 2020 ) focus on the impact of COVID‐19 on older workers in the United States. Using CPS data, they show that older workers who are over 65 years of age, especially women, are facing higher unemployment in this COVID‐19 recession compared to previous ones.

Kahn et al. ( 2020 ) show that firms in the United States dramatically reduced job vacancies from the second week of March 2020 and thereafter. The authors find that the job vacancy declines occurred simultaneously with increasing UI claims. Notably, the labor market declines (proxied through reductions in job vacancies and increases in UI claims) were uniform across states, with no notable differences across states which experienced the spread of the pandemic, or implemented stay‐at‐home orders, earlier than others. The study also finds that the reductions in job vacancies were uniform across industries and occupations, except for those in front line jobs, such as nursing. Baert et al. ( 2020a ) investigate the impact of COVID‐19 on career prospects through surveys conducted in Belgium. They document concerns that were expressed about job losses and missing out on promotions, especially among migrant workers.

Fairlie ( 2020 ) analyzes the impact of COVID‐19 on the number of small businesses in the United States. Using the April 2020 CPS data, the author finds that the number of active business owners declined by 22% between February and April 2020. While most major industries faced large drop in business, the authors also find that female and immigrant‐owned businesses were disproportionately affected.

With the enforcement of social distancing measures, work from home has become increasingly prevalent. The degree to which economic activity is impaired by such social distancing measures depends largely on the capacity of firms to maintain business processes from the homes of workers (Alipour et al. 2020 ; Papanikolaou & Schmidt, 2020 ). Additionally, working from home or working remotely are much more common and are thought to cause lower productivity losses in industries that are staffed by better educated and better paid workers (Bartik et al., 2020 ). Brynjolfsson et al. ( 2020 ) find that the increase in cases per 100k individuals is associated with a significant rise in the fraction of workers switching to remote work and the fall in the fraction of workers commuting to work in the United States. Interestingly, the authors find that people working from home are more likely to claim UI (if they are laid off) than people who are still commuting to work and are likely working in industries providing essential services.

Dingel and Neiman ( 2020 ) analyze the feasibility of jobs that can be done from home. They find that 37% of jobs can be feasibly performed from home. A different but related context for the feasibility of work from home is the extent to which the job involves face‐to‐face (F2F) interaction. According to Avdiu and Nayyar ( 2020 ), the job‐characteristic variables of home‐based work (HBW) and F2F interaction differ along three main dimensions, namely: (i) temporal (short run vs. medium run); (ii) the primary channel of effects (supply and demand of labor for the occupation/tasks); and (iii) the relevant margins of adjustment (intensive vs. extensive). They argue that the supply of labor in industries with HBW capabilities and low F2F interactions (e.g., professional, scientific, and technical services) might be the least affected. Nevertheless, those industries and occupations with HBW capabilities and high F2F interactions are likely to experience negative productivity shocks. As lockdown restrictions are lifted, industries with low HBW capabilities and low F2F interactions (e.g., manufacturing, transportation, and warehousing) might be able to recover relatively quickly. The risk of infection through physical proximity can be mitigated by wearing personal protective equipment (PPE) and by taking other relevant precautionary measures. However, those industries with low HBW capabilities and high F2F interactions (e.g., accommodation and food services, arts entertainment and recreation) are likely to experience slower recoveries, as consumers might be apprehensive about patronizing them, for example, cinemas and restaurants. Using a web survey in Belgium, Baert et al. ( 2020b ) find that a majority of respondents thought that teleworking and digital conferencing were here to stay and will become more common in the postpandemic period.

From the firm's perspective, there are large short‐term effects of temporary closures, such as the (perhaps permanent) loss of productive workers and declines in job postings, all of which are characterized by strong heterogeneity across industries. Bartik et al. ( 2020 ) survey a small number of firms in the United States and document that several of them have temporarily closed shop and reduced their number of employees compared to January 2020. The surveyed firms were not optimistic about the efficacy of the fiscal stimulus implemented by the federal government of the United States. Campello et al. ( 2020 ) find that job losses have been more severe for industries with highly concentrated labor markets (i.e., where hiring is dominated by a few employers), nontradable sectors (e.g., construction, health services), and credit‐constrained firms. Hassan et al. ( 2020 ) discern a pattern of heterogeneity with respect to firm resilience across industries around the World. Based on earnings call reports, they provide evidence that some firms are expecting increased business opportunities in the midst of the global disruption (e.g., firms which make medical supplies or others whose competitors are facing negative impressions after the outbreak due to their association with regions where case numbers are high). Barrero et al. ( 2020 ) measure the reallocation of labor in response to the pandemic‐induced demand response (e.g., increased hiring by delivery companies, delivery‐oriented restaurant/fast food chains, technology companies).

To conclude this subsection, a large number of studies try to predict labor market outcomes by exploiting high frequency data (e.g., Adams‐Prassl et al. 2020a , Chetty et al., 2020 ). For instance, Bartik et al. ( 2020 ) and Kurmann et al. ( 2020 ) rely on worker‐firm matched daily data drawn from “Homebase,” a scheduling and time clock software provider, to construct real‐time data for small businesses. Other studies have also used high‐frequency electricity market data to estimate the short‐run impacts of COVID‐19 on economic activity (e.g., Fezzi & Fanghella, 2020 ).

5.2. Health outcomes

The impact of the pandemic on physical health and mortality has been documented in many studies (e.g., Goldstein & Lee, 2020 ; Lin & Meissner, 2020 ). Knittel and Ozaltun ( 2020 ) document a positive correlation between the share of elderly population, the incidence of commuting via public transportation, and the number of COVID‐19 deaths in the United States. In contrast, the authors provide evidence that obesity rates, the number of ICU beds per capita, and poverty rates are not related to the death rate. Chatterji and Li ( 2021 ) document the effect of the pandemic on the US health care sector. The authors find that it is associated with a 67% decline in the total number of outpatient visits per provider by the week of April 12th‐18th 2020 relative to the same week in prior years. This might have negative health consequences, especially among individuals with chronic health conditions. Hermosilla et al. ( 2020 ) show that COVID‐19 has crowded out non‐COVID‐19‐related health care demands in China. Others, such as Alé‐Chilet et al. ( 2020 ), explore the drop in emergency cases in hospitals around the world.

Nevertheless, during a crisis, such as the COVID‐19 pandemic, it is common for everyone to experience increased levels of distress and anxiety, particularly the sentiment of social isolation (American Medical Association, 2020 ). A growing number of studies document worsening mental health status and levels of well‐being (Adams‐Prassl et al. 2020b ; Brodeur, Clark, Fleche, & Powdthavee, 2021 ; Davillas & Jones, 2020 ; de Pedraza et al., 2020 , and Tubadji et al., 2020 ). According to Lu et al. ( 2020 ), social distancing or lockdown measures are likely to affect psychological well‐being through a lack of access to essential household supplies, discriminatory treatment, or exclusion by neighbors. They assert that maintaining a positive attitude (in terms of severity perceptions, the credibility of real‐time updates of information, and confidence in social distancing measures) can help reduce depression. Hamermesh ( 2020 ) also provides evidence that, adjusted for numerous demographic and economic variables, happiness levels during the COVID‐19 pandemic are affected by how people spend time and with whom. In the opposite case, using an experimental set‐up, Bogliacino et al. ( 2020 ) find that a negative shock triggered by COVID‐19 lowers cognitive functionality and increases risk aversion and the propensity to punish others, that is negative reciprocity. Public mental health is also affected by the cognitive bias related to the diffusion of public death toll statistics (Tubadji et al., 2020 ). These needs are all the less likely to be addressed given the lower levels of provision of health care and social work services.

Using the Canadian Perspective Survey Series, Béland, Brodeur, Mikola, and Wright ( 2020 ) find that those who missed work not due to COVID‐19, and those who were already unemployed, showed declines in mental health. Using panel data in the United Kingdom, Etheridge and Spantig ( 2020 ) report a large deterioration in the state of mental health, with much larger effects for women.

The implementation of lockdown policy also adversely affected public mental health. Armbruster and Klotzbücher ( 2020 ) demonstrate that there were increases in the demand for psychological assistance (through helpline calls) due to lockdown measures imposed in Germany. The authors find that these calls were mainly driven by mental health issues such as loneliness and depression. Brodeur, Clark, Fleche, & Powdthavee et al. ( 2021 ) show that there has been a substantial increase in the search intensity on Google for “boredom” and “loneliness” during the postlockdown period in nine Western European countries and the United States during the first few weeks of lockdowns. Using experimental surveys, Codagnone Bogliacino, Gómez, and Charris et al. ( 2020 ) find that about 43% of the population in Italy, Spain, and United Kingdom are at high risk of developing mental health problems; not only because of the negative economic shock, but also due to conditions of long‐standing economic weakness and vulnerability in those countries.

Fetzer et al. ( 2020 ) find that there has been broad public support for COVID‐19 containment measures. However, some of the respondents believe that the general public fails to adhere to health measures, and that the governmental response has been insufficient. These respondents have a tendency to exhibit a poorer state of mental health. If governments are seen to take decisive actions, however, then the respondents altered their perceptions about governments and other citizens, which in turn improved their state of mental health.

5.3. Gender and racial inequality

A growing literature points out that COVID‐19 has had an unequal impact between genders and across races in OECD countries; specifically, women and racial minorities, such as African‐Americans and Latinos, have been unduly and adversely affected. While it is thought that prior recessions typically affected men more than women, many studies provide evidence that COVID‐19 has large negative effects on women's labor market outcomes (Adams‐Prassl et al., 2020a ; Forsythe et al., 2020 ; Yasenov, 2020 ). Alon et al. ( 2020 ) argue that women's employment is concentrated in sectors such as health care and education. Moreover, the closure of schools and daycare centers led directly to increased childcare needs, which would have a negative impact on working and/or single mothers. For example, based on household surveys in Spain, Farré et al. ( 2020 ) find that while men increased their participation in household work and childcare duties during lockdowns, the burden of these tasks fell disproportionately on women.

Couch et al. ( 2020a ) examine the variation in unemployment shocks among minority groups in the United States. The authors find that Latino groups were disproportionately affected by the pandemic. They attribute the difference to an unfavorable occupational distribution (e.g., Latino workers tend to work in nonessential services) and to lower skill levels among them. Borjas and Cassidy ( 2020 ) determine that the COVID‐19 shock led to a fall in employment rates of immigrant men compared to native men in United States, which was in contrast to the historical pattern observed during previous recessions. The immigrants’ relatively high rate of job loss was attributed to the fact that immigrants were less likely to hold jobs that could be performed remotely from home. The likelihood of being unemployed during March 2020 was significantly higher for racial and ethnic minorities in the United States (Montenovo et al., 2020 ). In a similar vein, McLaren ( 2020 ) finds that minorities’ population shares in a county strongly correlate with COVID‐19‐related deaths in the United States. After controlling for the factors of education, jobs, and travel patterns, the correlation holds for the African‐American and the First‐Nations populations. The author shows that these racial disparities between African‐Americans, First Nations peoples, and others can be partially attributed to differentials in public transit usage patterns.

Couch et al. ( 2020b ) find that COVID‐19 has unduly affected women compared to men in the United States. Using employment to population ratios and number of hours from the CPS data, the authors find that women with school‐age children faced greater declines in employment and work hours compared to men between April and August 2020. The reductions in work hours and employment can be explained by additional childcare responsibilities, job and skill characteristics, and lower numbers of women involved in “essential” industries.

Schild et al. ( 2020 ) find that COVID‐19 occasioned a rise of Sino‐phobia across the internet, particularly when western countries started showing signs of infection. Bartos et al. ( 2020 ) document the causal effect of economic hardships on hostility against certain ethnic groups in the context of COVID‐19 using an experimental approach. The authors find that the COVID‐19 pandemic magnifies sentiments of hostility and discrimination against foreigners, especially those from Asia.

5.4. Environmental outcomes

The global lockdown and the considerable slowdown of economic activities are expected to have a positive effect on the environment (Almond et al., 2020 ; Cicala et al., 2020 ). He et al. ( 2020 ) show that lockdown measures in China led to a remarkable improvement in air quality. The Air Quality Index and the fine particulate matter (PM 2.5 ) concentrations were brought down by 25% within weeks of the lockdown, with larger effects recorded in colder, richer, and more industrialized cities. Similarly, Almond et al. ( 2020 ) focused on air pollution and the release of greenhouse gases in China during the post‐COVID‐19 period. They determined that, while nitrogen dioxide (NO 2 ) emissions fell precipitously, sulphur dioxide emissions (SO 2 ) did not decrease. For China as a whole, PM 2.5 emissions fell by 22%; however, ozone concentrations increased by 40%. These variations show that there is not necessarily an unambiguous improvement in air pollution due to the economic slowdown. The reduction can be attributed to less travel in personal vehicles causing lower nitrous oxide (NO 2 ) emissions.

Brodeur, Cook, & Wright ( 2021 ) examine the causal effect of “safer‐at‐home” policies on air pollution across US counties. They find that “safer‐at‐home” policies decreased air pollution (measured as PM 2.5 emissions) by almost 25% on average, with larger effects for populous counties. Cicala et al. ( 2020 ) focus on the health and mortality benefits of reduced vehicle travel and electricity consumption in the United States due to stay‐at‐home policies, suggesting that reductions in emissions from less travel and from lower electricity usage reduced deaths by over 360 per month.

On the other hand, Andree ( 2020 ) focuses on the effect of pollution on cases, finding that PM 2.5 levels are a highly significant predictor of COVID‐19 incidence using data from 355 municipalities in the Netherlands. In terms of COVID‐19‐related deaths, Knittel and Ozaltun ( 2020 ) find no evidence that pollution levels are related to death rates in the United States.

Based on the research discussed in section  5 above, Table  3 provides a summary of these strands of the literature dealing with the socioeconomic and environmental outcomes resulting from social distancing actions, stay‐at‐home orders, and/or lockdowns including a listing of the statistical measures and methodologies that were utilized.

Socioeconomic outcomes of COVID‐19 lockdowns: Summary of studies


The economic literature deals with a wide assortment of policy measures. We organize our presentation into six broad topics: (i) the types of policy measures, (ii) the determinants of government policy, (iii) optimal testing methods, (iv) the lockdown measures and their associated factors, (v) the lifting of the lockdown measures, and (vi) the economic stimulus measures.

To mitigate the negative effects of public health controls on the economy and to sustain and promote public welfare, governments all around the World have implemented a variety of policies within a very short time frame. These include fiscal, monetary, and financial policy measures (Gourinchas, 2020 ). The economic measures vary across counties in terms of breadth and scope, and they target households, firms, health systems, and/or banks (Weder di Mauro, 2020 ).

Using a database of economic policies implemented by 166 countries, Elgin et al. ( 2020 ) employ the technique of Principal Component Analysis (PCA) to develop their COVID‐19 Economic Stimulus Index. The authors correlate the standardized index with predictors of governmental response, such as population characteristics (e.g., median age), public health‐related measures (e.g., the number of hospital beds per capita), and economic variables (e.g., GDP per capita). They find that the economic stimulus is larger for countries with higher COVID‐19 infections, older median ages, and higher GPD‐per‐capita levels. In addition, the authors develop a “Stringency Index,” which includes measures such as school closures and travel restrictions. They find that the “Stringency Index” is not a significant predictor of their economic stimulus index, which suggests that public health measures do not drive economic stimulus measures (Weder di Mauro, 2020 ).

On a similar note, Porcher ( 2020 ) has created an index of public health measures using the PCA technique. The index is based on 10 common public health policies implemented across 180 countries to mitigate the spread of COVID‐19. The index is designed to measure the stringency of the public health response across countries. The author finds that, abstracting from the COVID‐19 case numbers and deaths, countries which have better public‐health systems and effective governance tend to have less stringent public health measures.

C. Cheng et al. ( 2020 ) develop the “CoronaNet–COVID‐19 Government Response Database,” which accounts for policy announcements made by countries globally since 31 December 2019. The information that is contained in this data base is categorized according to: (i) type of policy, (ii) national versus subnational enforcement, (iii) people and geographic region targeted by the policy, and (iv) the time frame within which it is implemented. Table  4 provides a description of the government response database for 125 countries. Counts are tabulated according to 15 types of interventions for two variables: cumulative number of policies (of that type) implemented and the number of countries which have implemented it. It also displays that average value for the degree of enforcement.

Summary statistics of COVID‐19 government response dataset

Source: C. Cheng et al. ( 2020 ).

There is substantial variation across policy measures. The policy most governments have implemented is “external border restriction,” that is, restricting access to entry through ports. It has been imposed by 186 countries; the second most common policy measure, implemented by 169 countries, is “school closures.” However, in terms of the frequency of implementation across all countries, the type of “obtaining or securing health resources” has the highest level. This includes the provision of materials (e.g., face masks), personnel (e.g., doctors, nurses), and infrastructure (e.g., hospitals). The second most frequently implemented policy is “restrictions on nonessential businesses.” In terms of stringency of policy enforcement, “emergency declaration” and the formation of a “new task force” or an “administrative reconfiguration to tackle pandemic” are implemented with 100% stringency.

Due to these major differences between policy responses across countries and over time, the authors use a dynamic Bayesian item‐response approach to measure the implied economic, social, and political cost of implementing a particular policy over time. They also develop a supplementary measure labelled the “Policy Activity Index,” which assigns a higher rank for policy measures to countries that are more willing to implement a “costly” policy. Based on that index, the authors determine that school closure is the costliest to implement followed by mandatory business closure and social distancing policies. Moreover, internal border restrictions are viewed as more costly compared to external border restriction.

The topic of optimal testing methods has received a great deal of attention in the media and, to some extent, in academia. A well‐known proposal defended by Paul Romer and many others is a comprehensive “test and isolate” policy, which would effectively reduce the effective reproduction number and allow the economy to operate more openly. 14 Taipale et al. ( 2020 ) formalize this proposal and argue that the epidemic would collapse at a sufficient rate of testing and isolation, and that concurrent testing would outperform random sampling of individuals. Other proposals for optimal testing include regular testing of people in groups that are more likely to be exposed to COVID‐19 (e.g., Cleevely et al., 2020 ; Gollier & Gossner, 2020 ), multi‐stage group testing (e.g., Eberhardt et al., 2020 ), and testing on exit from quarantine instead of upon entry (e.g., Wells et al. 2020 ).

The topic of optimal lockdown policies has been investigated mostly by using epidemiology macroeconomic models, some of which are oriented around the dichotomy between the case in which the choices (and responses) are all made by private agents and the case in which the choices are made by a social planner (Acemoglu et al., forthcoming; Alvarez et al., forthcoming; Berger et al., forthcoming; Bethune & Korinek 2020 ; Eichenbaum et al., 2020 ). Jones et al. ( 2020 ) argue that in contrast to private agents, the social planner will seek to front‐load mitigation strategies: that is to impose strict lockdowns from the beginning to reduce the spread of infection and let the economy to fall into a deep recession. This is because their model's set‐up not only considers the concomitant health care costs and congestion in hospitals, but also rightly considers the fact that workers need time to become productive for a work‐from‐home situation. The outcomes are dependent on the assumed values of the parameters that are inputted into these models. The optimal policy choice reflects the rate of time preference, epidemiological factors, the value of statistical life, the rate at which death rate increases in the infected population, the hazard rate for a vaccine discovery, the learning effects in the health care sector, and the severity of output losses due to a lockdown (Gonzalez‐Eiras & Niepelt, 2020 ). The intensity of the lockdown depends on the gradient of the fatality rate as a function of the number of infected individuals and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown and shortens the duration of the optimal lockdown (Alvarez et al., forthcoming). Chang and Velasco ( 2020 ) argue that the optimality of policies depends on peoples’ expectations. For instance, fiscal transfers must be large enough to induce people to stay at home in order to reduce the degree of contagion; otherwise they might not change their behavior in efforts to reduce the risk of infection. Their analysis also contains a critique of the use of SIR models, as the parameters used in that class of models (which remain fixed in value) would shift as individuals change their behavior in response to policy. Kozlowski et al. ( 2020 ) investigate the scarring effect on perceptions (i.e. the change in belief about the probability of an extreme but negative or tail‐risk event) of COVID‐19, and find that revisions in belief about tail‐risk events among economic agents will lead to a larger and more persistent negative impact on the economy in the long run.

When the daily death rates and case numbers decline, policies regarding reopening the economy are of primary importance. Gregory et al. ( 2020 ) describe the lockdown measure as a “loss of productivity,” whereby relationships between employers and laborers are suspended, terminated, or continued. They further explain that postpandemic, the speed and the type (V‐shaped or L‐shaped) of recovery depend on at least three factors: (i) the fraction of workers who, at the beginning of the lockdown, enter unemployment while maintaining a relationship with their employer, (ii) the rate at which inactive relationships between employers and employees dissolve during the lockdown, and (iii) the rate at which workers who, at the end of the lockdown, are not recalled by their previous employer can find new, stable jobs elsewhere (Gregory et al., 2020 ).

Harris ( 2020 ) points out the importance of utilizing several status indicators (e.g., results of partial voluntary testing, guidelines for eligibility of testing, daily hospitalization rates) in order to decide upon the course of action on reopening the economy. Kopecky and Zha ( 2020 ) state that decreases in deaths are either due to implementation of social distancing measures or to herd immunity; it is hard to identify and disentangle those factors using standard SIR models. They argue that with the “identification problem,” there will be considerable uncertainty about the conditions for restarting the economy. Only comprehensive testing can help resolve this ambiguity by quickly and accurately identifying new cases so that future outbreaks could be contained by isolation and contact‐tracing measures (Kopecky & Zha, 2020 ).

Agarwal et al. ( 2020 ) rely on synthetic control methods to investigate the effect of counterfactual mobility restriction interventions in United States. Using the daily death data from different countries, the authors create different “synthetic mobility United States” variables. These are applied to predict a counterfactual scenario and to understand the trade‐off between different levels of mobility interventions on death levels in United States. They find that a small decrease in mobility reduces the number of deaths; however, after registering a 40% drop in mobility, the benefits derived from mobility restrictions (in terms of the number of deaths) diminish. Using a counterfactual scenario, the authors find that lifting severe mobility restrictions but retaining moderate mobility restrictions (e.g., by imposing limitations in retail and public transport locations) might effectively reduce the number of deaths in United States. Others, such as Rampini ( 2020 ), make the case for the sequential lifting of lockdown measures for the younger population at the initial stages, followed by the older population at later stages. The authors state that the lower effect on the younger population group is a fortunate coincidence, and thus, the economic consequences of interventions can be greatly reduced by adopting a sequential approach. Oswald and Powdthavee ( 2020 ) make a similar case for releasing the younger population from mobility restrictions first on the grounds of higher economic efficiency (as they are more likely to be in the labor force) and their greater resilience against infections.

As some US states reopened, some researchers turned their focus on the immediate consequences. Nguyen et al. ( 2020 ) find that 4 days after reopening, mobility has increased by 6 to 8%, with greater increases across states which were late adopters of lockdown measures. These findings have important implications for the resurgence of cases, hospital capacity utilization, and further deaths. Dave, Friedson, Matsuzawa, and McNichols et al. ( 2020 ) analyze the effect of lifting the shelter‐in‐place order in Wisconsin, after the Wisconsin Supreme Court abolished it, on social distancing and the number of cases and find no statistically significant impact. W. Cheng et al. ( 2020 ) find that employment activity in the United States increased in May due to reopening in some states, mainly as a result of people who resumed working at their previous job. However, they find that the longer employees are separated from their firms, the more their re‐employment probabilities decline.

In regards to the aggregate macroeconomy, Gourinchas ( 2020 ) states that without substantial, timely, and stimulative macroeconomic intervention, the output lost from the economic downturn will be greatly amplified, especially as economic agents react to the negative shock by reducing consumption spending, investment spending, and engaging in lower credit transactions. The author suggests that there should be cross‐regional variation in government responses based on country characteristics. With high amounts of government debt and historically low interest levels existing in most developed countries, Bianchi et al. ( 2020 ) recommend a coordinated monetary and fiscal policy to address the COVID‐19 economic fallout. They recommend that fiscal policy should be used to enact an emergency budget with a ceiling placed on the debt‐to‐GDP ratio. This measure would increase aggregate spending, raise the inflation rate, and reduce real interest rates. The monetary authorities would need to coordinate with fiscal policy authorities by adopting an above‐normal inflation target. In the long run, governments would try to balance the budget, and future monetary policy would aim to bring inflation back to normal levels.

Bigio et al. ( 2020 ) focus on the cases for government transfers versus credit subsidy policies. They determine that the optimal mix between them depends on the level of financial development in the economy. According to these authors, economies with a developed financial system should utilize stimulative credit policies. On the other hand, developing economies should engage in more transfer spending. Guerrieri et al. ( 2020 ) show that the optimal economic policy response for the “Keynesian supply shock” induced by COVID‐19 would be to combine expansionary monetary policy and bolster social insurance programs for employees in the affected sectors. Unconventional policies, such as wage subsidies, helicopter drops of liquid assets, equity injections, and loan guarantees, can keep the economy in a full employment, high‐productivity equilibrium (Céspedes et al., 2020 ). These policies can break the cycle of negative feedback loops between corporate defaults and potential insolvency of financial intermediaries, which could culminate in a meltdown in financial markets (Elenev et al., 2020 ). Didier et al. ( 2020 ) discuss the policy menu, priorities, and trade‐offs of providing direct financing to firms.

Chetty et al. ( 2020 ) analyze the causal effect of policies implemented in the United States on households and businesses. They find that stimulus payments delivered through the Coronavirus Aid, Relief, and Economic Security (CARES) Act increased consumption spending, and that this spending was directed toward durable goods, which require low physical interaction at various stages of production. As a result, this spending is not directed toward small‐ and medium‐size businesses whose revenues were very adversely affected. On the other hand, loans to small businesses from the Paycheck Protection Program did little to restore employment among businesses. According to their analysis, the economic recovery depends on restoring consumer confidence and targeting income replacement programs rather than uniform lump‐sum stimulus payments.

Codagnone, Bogliacino, and Gómez, Folkvord et al. ( 2020 ) focus on the expectations of stakeholders with regards to the postlockdown period. Using an experimental survey in Spain, Italy, and United Kingdom, the authors find that exposure to the COVID‐19 shock and the ensuing lockdown led to pessimistic expectations about job opportunities, greater drawdowns of savings than before, and a deterioration in social relations which might be instrumental in finding job opportunities in the long run. The authors conclude that the fiscal policy measures might be insufficient in managing these expectations amidst uncertainties. They call on policy makers to draft contingency plans for exiting the lockdown—not only in terms of public expenditures earmarked for postlockdown operations, but also in terms of public health strategies to tackle a second wave of COVID‐19.


This study delved into the research related to the economics of COVID‐19 that has been released over a short time period. Our primary aim is to synthesize and to bring coherence and structure to the very rapidly growing body of relevant scientific evidence. By providing an annotated list of dozens of articles along with a brief capsule of their content, we hope to facilitate further research in the many strands of the COVID‐related literature. For readers who are interested in this critically important and pressing topic, this piece also provides an informative summary of the state of knowledge at the time of writing.

Before covering the impacts of COVID‐19, we documented the most popular data sources that are exploited to measure the known cases and deaths resulting from COVID‐19, as well as the social distancing activities. We first pointed out that the numbers of reported cases and deaths are subject to measurement error due to many factors, including testing capacity constraints and lags. Mobility measures that are based on GPS coordinates emitted from cell phones have been used extensively to measure social distancing. However, there are certain caveats that apply, particularly in terms of privacy concerns and the representativeness of data. The article also reviewed separate research related to social distancing activity itself, particularly in regards to its determinants, its efficacy in mitigating the spread of COVID‐19, and compliance with these orders. Going forward, social distancing actions and their measurements will continue to figure prominently in academic research and policy development.

We divided our coverage of the impact of the macroeconomy into two subsections, the first of which deals with the propagation mechanisms. The stay‐at‐home orders have very adverse effects on supply chains as well as on employment, which in turn causes drastic declines in consumption spending for many goods and services. The resultant declines in consumer and investor confidence reinforce negative multiplier effects in a downward spiral between labor and output markets, which can be partially attenuated by stimulative fiscal and monetary policies. Since the trajectory for the macroeconomy depends critically on the degree of spread of the virus itself, some researchers have integrated that element into their models. We reviewed the three potential “shapes” for the macroeconomic recovery: the highly optimistic yet implausible “V” path, the somewhat favorable “U” path, and the pessimistic yet more likely “L” path.

The second aspect of the macroeconomic impact of COVID‐19 that we discussed involves the forecasts. It is thought that the lockdown and social distancing measures wreak greater economic harm than the spread of the virus itself. The tremendous uncertainty regarding the path of the virus is compounded with a high degree of economic uncertainty such that these projections are subject to very wide confidence intervals and constant revisions. Some articles have attempted to address the longer‐term negative impacts on macro variables such as capital formation, productivity, and government finances. Other studies have focused on changes in patterns of consumption, employment, savings, and consumer debt by exploiting real‐time data.

In terms of the socioeconomic consequences of COVID‐19, we focus on the impact of the pandemic and the social distancing measures on outcomes in four areas: the labor market, mental health and well‐being, racial and gender inequality, and the environment. In terms of the labor market outcomes, research has shown that there is a high degree of heterogeneity in the pattern of job losses. The pandemic has caused a major shift toward work from home and away from positions involving F2F interactions with either the public or coworkers. Due to technological features and the nature of the services rendered, there are only a certain number of jobs that can be “feasibly” done from home and do not require F2F interactions. This contributes to the disproportionate effect of the pandemic on workers in certain industries and occupations, many of which have a relatively high concentration of lower‐skilled and/or less educated workers.

Social distancing measures have led to serious deteriorations in the levels of mental health, family stress, and domestic violence. Health care services for non‐COVID patients have been crowded out in many instances. There has been a marked rise in observed racial discrimination and sentiments of hostility toward certain ethnic groups. A growing number of studies also document that women have been adversely affected by the loss of child care and educational services for their children. The only seemingly positive consequence of social distancing/lockdown measures is the decrease in air pollution levels and the incidence of accidents involving motor vehicles. However, the impacts on the environment are multi‐faceted, and thus there remains a fair amount of ambiguity.

The goal of our piece was to survey and summarize the findings of the literature on the economics of COVID‐19. This was a very challenging task, as the literature is growing and evolving fast, and the pandemic is far from over at the time of writing. There are a few qualifications that are worth mentioning. First, very few of the research articles surveyed have undergone normal scientific review processes. Second, we mostly did not comment on methodology, which necessitates caution in interpretations. Finally, due to time as well as space constraints, we offer little in the way of critical analysis. Nonetheless, we hope this survey will facilitate further research in the many strands of the COVID‐related literature.

Supporting information

Table A: Major Pandemics: Historical Timeline

Brodeur A, Gray D, Islam A, Bhuiyan S. A literature review of the economics of COVID‐19 . Journal of Economic Surveys . 2021; 35 :1007–1044. 10.1111/joes.12423 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

1 Social distancing (or physical distancing) is defined as maintaining physical space between yourself and other people residing outside one's home. To practice social/physical distancing: (i) stay at least 6 feet (about 2 arms’ lengths) from other people, (ii) do not gather in groups, and (iii) avoid crowded places and mass gatherings.

2 The list of NBER working article is available at this URL: https://www.nber.org/wp_covid19.html

3 The list of IZA discussion articles is available at this URL: https://covid‐19.iza.org/publications

4 See the link for the numbers and visual representation. Retrieved from https://coronavirus.jhu.edu

5 See WHO COVID‐19 Dashboard: https://covid19.who.int.

6 Refer to Johns Hopkins University ( 2020b ) for CFR data across countries.

7 See the link for further details: https://ourworldindata.org/mortality‐risk‐covid .

8 See the link for further details: https://covidtracking.com/data .

9 See the link for further details: https://covidtracking.com/race .

10 Mobility measures track work locations based on movements to a workplace from a reference point such as their home. However, if a person works from home or becomes unemployed, there will not be a distinct workplace reference point. Hence, the quality of mobility measures is expected to deteriorate.

11 The WHO Health System Response Monitor provides a cross country analysis and other details: https://analysis.covid19healthsystem.org/ .

12 According to the authors, if all members of a set choose to implement shelter‐in‐place policies, then the best response for agents is to follow. Hence, even in the absence of a federal mandate, the members of this “tipping set” can drive all others to adopt shelter‐in‐place policies.

13 The interaction between economic and epidemiological models is described in more details in the Online Appendix.

14 Further details on the “test and isolate” policy is available at the URL: https://paulromer.net/covid‐sim‐part1.

15 See the link for further details: https://www.google.com/covid19/mobility .

16 See the link for further details: https://www.unacast.com/covid19 .

17 See the link for further details: https://www.safegraph.com/dashboard/covid19‐commerce‐patterns .

18 See the link for further details: http://research.baidu.com/Blog/index‐view?id=13 .

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